Interobserver error in grassland vegetation surveys: sources and implications

人类观测误差是植被测量中不可避免的一个问题。我们量化了与高草草原植被长期监测相关的观测者间误差的四个组成部分：忽略误 差、误识别误差、谨慎误差和估计误差。由于观察者会产生误差，我们还评估了地块大小与伪周转率的关系，以及对比了物种组成和丰度的伪变化与四年间植被变化之间的关系。这项研究是在美国堪萨斯州的高草草原国家保护区进行的。监测点包括10个地块，每个地块由一系列的四个嵌套框架(0.01, 0.1, 1和10 m²)组成。在每个嵌套框架中记录了所有的草本物种，并且在10 m²的空间尺度下，视觉估计了7个覆盖类别内的叶面覆盖。总共调查了300个地块(30个地点)，并随机选择28个地块重新进行测量以评估观测者的误差。所有的调查由四名观测者分两组完成。研究结果表明，在10 m²空间尺度上，由忽略误差引起的伪周转率平均为18.6%，而由误识别误差和谨慎误差引起的伪周转率平均值分别为1.4%和0.6%。尽管由重新定位引起的误差可能也起一定的作用，由忽略误差导致的伪周转率随样地面积的减小而增 加。物种组成在四年期间的变化(排除潜在的误识别误差和谨慎误差)为30.7%，其中包括由忽略误差和实际变化引起的伪周转率。18.6%的忽略误差表明四年期间的实际变化只有12.1%。对于估计误差，26.2%会记录为不同的覆盖等级。在四年的时间内，46.9%的记录显示了不同的覆盖等级，这表明两个时间段间覆盖率变化的56%是由于观测者误差造成的。     

非正态分布预测模型误差的估计

提出了模型预测误差在其分布为非正态分布时的区间估计方法,研究了模型预测误差的估计问题,给出了应用实例.     

Measurement error: Inter-and Intraobserver Variability. An Empiric Study

The intra- and inter-observer measurement error variability was studied using univariate and multivariate statistical tests. Eleven skeletal variables of four individuals each in four Primate species were measured ten times by three different researchers, using six different tools. An average measurement error of 0.52 mm. was obtained. Univariate statistics showed significant differences among reseachers. A multivariate discriminant analysis could also discriminate them. The measurement error may be either systematic or random, and depends not only on the researcher, but also on the tool used, the variable measured, and on the magnitude of the variable. The technique of Measurement Replication is proposed in order to reduce the measurement error, specially when compairing small samples or when trying to find small average differences between populations. The replication technique also reduces the standard deviation of the population sample.     

On the Performance of Some Classification Rules for Qualitative Data for Simulated Underlying Distributions

Two linear functions for discriminating with qualitative variables (Fisher's linear discriminant function and the independence rule) are compared with the general multinomial procedure, a rule based on Lancaster's definition of higher order interactions and the quadratic discriminant function. The evaluation of these functions is carried out within Monte Carlo experiments. Various types of underlying distributions generated by a special algorithm are used.     

Influence of IMU position and orientation placement errors on ground reaction force estimation

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.     

Dosimetric impact of uncorrected systematic yaw rotation in VMAT for peripheral lung SABR

AimThis study aimed to evaluate the dosimetric impact of uncorrected yaw rotational error on both target coverage and OAR dose metrics in this patient population.BackgroundRotational set up errors can be difficult to correct in lung VMAT SABR treatments, and may lead to a change in planned dose distributions.Materials and methodsWe retrospectively applied systematic yaw rotational errors in 1° degree increments up to −5° and +5° degrees in 16 VMAT SABR plans. The impact on PTV and OARs (oesophagus, spinal canal, heart, airway, chest wall, brachial plexus, lung) was evaluated using a variety of dose metrics. Changes were assessed in relation to percentage deviation from approved planned dose at 0 degrees.ResultsTarget coverage was largely unaffected with the largest mean and maximum percentage difference being 1.4% and 6% respectively to PTV D98% at +5 degrees yaw.Impact on OARs was varied. Minimal impact was observed in oesophagus, spinal canal, chest wall or lung dose metrics. Larger variations were observed in the heart, airway and brachial plexus. The largest mean and maximum percentage differences being 20.77% and 311% respectively at −5 degrees yaw to airway D0.1cc, however, the clinical impact was negligible as these variations were observed in metrics with minimal initial doses.ConclusionsNo clinically unacceptable changes to dose metrics were observed in this patient cohort but large percentage deviations from approved dose metrics in OARs were noted. OARs with associated PRV structures appear more robust to uncorrected rotational error.     

度量误差对模型参数估计值的影响及参数估计方法的比较研究

基于模型V=aDb,首先在Matlab下用模拟实验的方法,研究了度量误差对模型参数估计的影响,结果表明:当V的误差固定而D的误差不断增大时,用通常最小二乘法对模型进行参数估计,参数a的估计值不断增大,参数b的估计值不断减小,参数估计值随着 D的度量误差的增大越来越远离参数真实值;然后对消除度量误差影响的参数估计方法进行研究,分别用回归校准法、模拟外推法和度量误差模型方法对V和D都有度量误差的数据进行参数估计,结果表明:回归校准法、模拟外推法和度量误差模型方法都能得到参数的无偏估计,克服了用通常最小二乘法进行估计造成的参数估计的系统偏差,结果进一步表明度量误差模型方法优于回归校准法和模拟外推法．     

Set-up uncertainty during postmastectomy radiotherapy with Segmented Photon Beams Technique

Aim

To verify the reproducibility of patients irradiated after mastectomy on the immobilization system designed and manufactured for our hospital and to compare the Internal Protocol (IP) with the modified-No Action Level Protocol.

Background

Application of forward IMRT techniques requires a good reproducibility of patient positioning. To minimize the set-up error, an effective immobilization system is important.

Materials and methods

The study was performed for two groups of 65 each. In the first group, portal images for anterior field were taken in 1–3 fractions and, subsequently, three times a week. In this group, the mNAL protocol was used. In the second group, the IP was used. The portal images from the anterior field and from the gantry 0 were taken during the 1–3 and 10 fractions. In both groups, image registration was performed off-line. For each group the systematic and random errors and PTV margin were calculated.

Results

In the first group the value of the population systematic errors and random errors were 1.6 ± 1.6 mm for the left–right, and 1.5 ± 1.7 mm for the cranial–caudal directions, respectively, 1.7 ± 1.3 mm, and 1.9 ± 1.3 mm for the second group. The PTV margins for the left–right and cranial–caudal directions were 5.1 and 4.9 mm for the first group and 5.4 and 6.4 mm for the second group.

Conclusions

For patients immobilized with our support device treated according to the mNAL protocol or IP, a good set-up reproducibility was obtained. Implementation of IP limits the number of required images.     

Evaluation of expected absolute error affecting the maximum specific growth rate for random relative error of cell concentration

Borzani's [(1994) World Journal of Microbiology and Biotechnology 10, 475–476] idea of evaluation of absolute error affecting the 'maximum specific growth rate' (ESGR), calculated on the basis of the first and the last time points of the entire experimental time period, is generalized to the real-life situations where the relative errors of cell concentration cannot be assumed to be constant during the experiment. Visualizing the entire experimental time period as to comprise of several successive, mutually exclusive and exhaustive time intervals, we compute specific growth rates (SGRs) for each of these time intervals. Defining maximum of these SGR values as MSGR in contrast to Borzani's ESGR our aim is to study the effect of the expected absolute error on SGRs of different intervals. This will reveal the discrepancy between the true and observed MSGRs. Assuming the relative error distribution on (0,1) to be rectangular and symmetric truncated normal with mean at 0.5 and suitable variance, the expected values of the absolute errors are evaluated and numerically tabulated using the software packages MATHEMATICA and S-PLUS. Our results thus hold for situations involving varying relative errors where Borzani's results cannot be applied. A discussion with a concrete numerical example on the misidentification of the MSGR interval due to the effect of the random relative measuremental errors reveals to an experimental biologist that ignorance of this fact may lead to his/her entire experiment being futile.     

Linear constrain relations in biochemical reaction systems III. Sequential application of data reconciliation for sensitive detection of systematic errors

This article presents a method to test the presence of relatively small systematic measurement errors; e.g., those caused by inaccurate calibration or sensor drift. To do this, primary measurements-flow rates and concentrations-are first translated into observed conversions, which should satisfy several constraints, like the laws of conservation of chemical elements. This study considers three objectives: 1.Modification of the commonly used balancing technique to improve error sensitivity to be able to detect small systematic errors. To this end, the balancing technique is applied sequentially in time.2.Extension of the method to enable direct diagnosis of errors in the primary measurements instead of diagnosing errors in the observed conversions. This was achieved by analyzing how individual errors in the primary measurements are expressed in the residual vector.3.Derivation of a new systematic method to quantitatively determine the sensitivity of the error, is that error size at which the expected value of the chisquare test function equals its critical value.The method is applied to industrial data demonstrating the effectiveness of the approach. It was shown that, for most possible error sources, a systematic errors of 2% to 5% could be detected. In given application, the variation of the N-content of biomass was appointed to be the cause of errors. (c) 1994 John Wiley & Sons, Inc.     

蛋白质序列与EST序列的反翻译联配

随着大规模技术的进步,收录到数据库中的序列很快,其中大多是未知功能的ＥＳＴｓ（表达序列标签,Ｅｘｐｒｅｓｓｅｄ Ｓｅｑｕｅｎｃｅ Ｔａｇｓ）,一般通过蛋白南－ＥＳＴ序列联配来实验ＥＳＴ的功能提示。由于ＥＳＴ含有５％左右的误差,特别严重的是其中的移框误差,用通常的方法将ＥＳＴ按６个框翻译为蛋白南序列再进行联配难以处理移框误差问题。通过考虑ＥＳＴ序列各种可能的误差,将氨基酸序列反翻译为核苷酸序列,在核     

Uncertainty analysis of eddy flux measurements in typical ecosystems of ChinaFLUX

Fluxes of CO₂ (FCO₂) and energy (latent heat, LE; sensible heat, H) exchange between ecosystems and atmosphere, as measured by the eddy covariance technique, represent a fundamental data source for global-change research. However, little is known about the uncertainties of flux measurements at an ecosystem level in China. Here, we use data from six eddy covariance tower sites in ChinaFLUX, including two forested sites, three grassland sites, and one agricultural site, to conduct a cross-site analysis of random flux errors (RFEs) of FCO₂, LE, and H. By using the daily-differencing approach, paired observations are obtained to characterize the random error in these measurements. Our results show that: (1) The RFEs of FCO₂, LE, and H in different ecosystems of ChinaFLUX closely follow a double-exponential (Laplace) distribution, presumably due to a superposition of Gaussian distribution for high flux magnitude. (2) The RFEs of FCO₂, LE, and H are not homogeneous and appear to be a linear function of flux magnitude. (3) Except for H, the RFEs of FCO₂ and LE exhibit a distinct seasonal pattern. For FCO₂, the dependence of RFEs on wind speed varies somewhat according to vegetation type, whereas for LE and H, there is no such dependence. The effect of temperature on RFEs is not statistically significant (P < 0.05). Both the distribution and the relationship of RFEs with flux magnitude in ChinaFLUX are essentially in accord with those in AmeriFlux and CarboEurope.     

Characterizing,Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches

Life‐cycle assessment (LCA) practitioners build models to quantify resource consumption, environmental releases, and potential environmental and human health impacts of product systems. Most often, practitioners define a model structure, assign a single value to each parameter, and build deterministic models to approximate environmental outcomes. This approach fails to capture the variability and uncertainty inherent in LCA. To make good decisions, decision makers need to understand the uncertainty in and divergence between LCA outcomes for different product systems. Several approaches for conducting LCA under uncertainty have been proposed and implemented. For example, Monte Carlo simulation and fuzzy set theory have been applied in a limited number of LCA studies. These approaches are well understood and are generally accepted in quantitative decision analysis. But they do not guarantee reliable outcomes. A survey of approaches used to incorporate quantitative uncertainty analysis into LCA is presented. The suitability of each approach for providing reliable outcomes and enabling better decisions is discussed. Approaches that may lead to overconfident or unreliable results are discussed and guidance for improving uncertainty analysis in LCA is provided.     

A Multistage Procedure for Analyzing Unreplicated Factorial Experiments

A multistage procedure, which is based on the likelihood principle, is proposed to identify active effects in unreplicated factorial designs and their fractions. The proposed procedure controls the experimental error rate (EER) at any prespecified level in industrial and biomedical experiments. Extensive comparison with Lenth 's (1989) test is discussed.     

Sources of uncertainty in the quantification of genetically modified oilseed rape contamination in seed lots

Testing of seed and grain lots is essential in the enforcement of GM labelling legislation and needs reliable procedures for which associated errors have been identified and minimised. In this paper we consider the testing of oilseed rape seed lots obtained from the harvest of a non-GM crop known to be contaminated by volunteer plants from a GM herbicide tolerant variety. The objective was to identify and quantify the error associated with the testing of these lots from the initial sampling to completion of the real-time PCR assay with which the level of GM contamination was quantified. The results showed that, under the controlled conditions of a single laboratory, the error associated with the real-time PCR assay to be negligible in comparison with sampling error, which was exacerbated by heterogeneity in the distribution of GM seeds, most notably at a small scale, i.e. 25 cm³. Sampling error was reduced by one to two thirds on the application of appropriate homogenisation procedures.     

Observer error in vegetation surveys: a review

Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error. Three types of errors are common: (i) overlooking error (i.e. not observing species actually present), (ii) misidentification error (i.e. not correctly identifying species) and (iii) estimation error (i.e. not accurately estimating abundance). I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies.Important findings Almost all studies (92%) that tested for a statistically significant effect of observer error found at least one significant comparison. In surveys of species composition, mean pseudoturnover (the percentage of species overlooked by one observer but not another) was 10–30%. Species misidentification rates were on the order of 5–10%. The mean coefficient of variation (CV) among observers in surveys of vegetation cover was often several hundred % for species with low cover, although CVs of 25–50% were more representative of species with mean covers of>50%. A variety of metrics and indices (including commonly used diversity indices) and multivariate data analysis techniques (including ordinations and classifications) were found to be sensitive to observer error. Sources of error commonly include both characteristics of the vegetation (e.g. small size of populations, rarity, morphology, phenology) and attributes of the observers (e.g. mental fatigue, personal biases, differences in experience, physical stress). The use of multiple observers, additional training including active feedback approaches, and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys.     

Sitting height: An analysis of five measurement techniques

Five methods of obtaining sitting height were compared by taking triple trials of each. Three methods required that the subjects be seated on a table, the fourth had them sitting on the floor against a wall, and the fifth placed them in a recumbent position. Three trials of the first method were obtained without stretching and with the feet unsupported. These were followed by stretching techniques with subjects seated on the table, first with the feet unsupported and then with them supported, then with the subjects sitting on the floor, and finally with the subjects in the recumbent position. All methods were shown to be precise, with the smallest absolute measurements and highest technical error of measurement noted in the floor seated method. There was a tendency for females to yield greater values from being stretched, and for males to have greater values in the recumbent position.     

Estimation of Precision in DLW Studies Using the Two-Point Methodology

Previous attempts to estimate precision of doubly labeled water (DLW) estimates of CO₂ production, using propagation of error analyses, have necessarily made simplifying assumptions which may compromise the resultant error estimate. Using an empirical iteration approach, error distributions for the DLW calculation were generated which overcome these problems. The error distribution for CO₂ estimates generated by DLW is symmetrical but not normal. The distribution is significantly truncated such that the 99% inclusion limits are 2.034 SD and not 2.58 SD. The precision error (99% CI for mean as percent of the mean) in DLW experiments, when using duplicate analyses, varies between approximately 3% and 47% depending on the ratio of the elimination constants of the two labels (k_o/k_d), experimental duration and initial isotope dose. The error could be improved by approximately 10 fold by increasing the number of replicates at all six isotope determinations from 2 to 5. Estimating precision in actual experiments can be made using the same empirical approach. The resultant estimates can be of extreme value in evaluating the role of precision as a factor influencing deviations during validation studies, and also for weighting mean estimates in applications of the technique. The deviations of DLW estimates from those made simultaneously by indirect calorimetry in a small mammal validation study were generally greater than the precision of the DLW estimates of CO₂ production. This may indicate there are more problems with the technique than precision alone.