泥河湾盆地南山边遗址发现的旧石器

南山边遗址位于泥河湾盆地东缘、大田洼台地北缘,于2013年的旧石器调查中发现。随后的试掘中,出土石制品38件,动物化石3件,石制品包括石核、石片、刮削器、残片、断块等,属于中国北方以小石制品为主的主工业类型。石制品位于泥河湾层中下部,根据地层对比,与大长梁地点、小长梁遗址、照坡遗址、葡萄园遗址等属于同一层位,时代相同,由此在台地北缘形成一处面积广阔、时代接近、分布集中的遗址区,为探讨早更新世古人类的活动范围、栖居模式、遗址功能分析等提供有益线索。     

湖北郧县肖沟旧石器时代遗址发掘简报

肖沟遗址位于湖北省郧阳区安阳镇钟家河村,埋藏于丹江口库区汉江左岸第二级堆积阶地,遗址所在地三面环水,北靠低山。2010年10月-12月对该地点进行抢救性发掘,揭露面积近1500 m~2,出土石制品413件。石制品类型包括石器、石核、石片、断块断片和碎块碎片等。古人类利用河流砾石,就地取材加工石制品;硬锤锤击是主要剥片方法;石制品总体以小型居多,石器主要以石片为毛坯,边刮器为主要类型。石制品特点显示石片工业特点。石制品类型以石片类和断块类为主体,石制品拼合率近10%,是一处原地埋藏的古人类遗址。通过与丹江口库区相关旧石器遗址的比较,结合地貌地层特点,推测该遗址形成于旧石器时代晚期。     

贵州省中部和西南部新发现的洞穴遗址调查简报

贵州以更新世洞穴遗址发育在我国古人类学和旧石器时代考古学领域占有重要地位。本文对近年来贵州中部和西南部洞穴遗址的调查发现进行报道。新发现的9处洞穴遗址均分布在珠江水系的盘江流域,文化遗物主要为石制品。黔中遗址的石制品原料主要为燧石和硅质灰岩,而黔西南石制品原料多以河流成因的石英岩砾石居多;剥片技术主要为锤击法;黔中石制品的个体较小,而黔西南石制品的个体相对较大;石器组合以刮削器占多数。区域对比显示这些遗址的年代大致在距今3万年后的更新世末期至全新世初期。本次调查丰富了更新世末期古人类在贵州的活动信息,其中惠水清水苑大洞、长顺马路大洞和安龙路边大洞具有较大的工作前景。     

广西百色公篓遗址石制品的初步研究

公篓遗址位于广西百色市田阳县境内, 地处百色盆地右江南岸第四级阶地, 地理坐标为23°45.568'N, 106°42.210'E。该遗址最早发现于上世纪八十年代, 当时采集石制品88件, 是百色盆地较早发现的一处旧石器时代遗址; 2010年我们对遗址进行复查时又采集石制品14件, 本文对两次采集的石制品进行了分析。石制品类型有石核、石片、砍砸器、手镐、刮削器、手斧和断块, 其中砍砸器数量最多; 原料来源于遗址附近第四级阶地的砾石层, 有石英岩、硅质岩、石英、粉砂岩、细砂岩和角砾岩6种, 以石英岩为主; 石制品以大型和中型为主, 也有一定数量的小型石制品; 剥片和修理方法主要为锤击法。根据地貌和地层可以判断该遗址年代为中更新世早期。     

贵州观音洞遗址石制品剥片技术辨析

近期,英国杂志《自然》（Nature）发表了对贵州观音洞遗址1964-1973年出土石制品的再研究结果,认为该遗址石制品中存在系统的勒瓦娄哇技术;新的光释光测年显示遗址主文化层距今17-8万年。据此,该文作者认为观音洞遗址的石制品组合代表了东亚最早的勒瓦娄哇技术遗存。本文对观音洞遗址石制品的研究历史、勒瓦娄哇技术研究简史和定义等进行了回顾和梳理,同时对观音洞遗址相关石制品进行了详细观察分析。在此基础上认为,观音洞遗址石制品组合是一个相对简单的石核-石片技术复合体,与之前学者得出的结论一致;不存在勒瓦娄哇技术。最后,本文对宁夏水洞沟、金斯太、通天洞等遗址发现的勒瓦娄哇技术遗存的简要分析表明,目前尚无可靠证据显示中国在距今5万年前出现过勒瓦娄哇技术。     

丹江口库区水牛洼旧石器遗址发掘简报

水牛洼遗址位于湖北省丹江口市均县镇关门岩村,是汉水流域一处同时具有旧石器早期与晚期文化层的重要遗址。2010年3~4月间,吉林大学边疆考古研究中心对该遗址进行了发掘,揭露面积675m2,共获石制品301件,类型包括石核、石片、断块及工具等。地貌和地层对比显示,遗址年代大致为中更新世至晚更新世晚期。遗址可分为上、下两个文化层。上文化层出土石制品246件,属晚更新世;下文化层出土石制品55件,处于中更新世。下文化层石制品特点显示了中国南方砾石石器工业的特点,而上文化层却出现了中国北方石片石器工业的文化因素。     

鄂尔多斯乌兰木伦遗址石制品拼合研究及其对遗址成因的指示意义

乌兰木伦遗址是继上世纪20年代发现萨拉乌苏和水洞沟遗址以来,在鄂尔多斯高原时隔近90年新发现的重要旧石器遗址。自2010年发现以来,该遗址已进行了5个年度的连续性发掘,发现了丰富的石制品、动物化石以及用火遗迹等。光释光和碳14年代测定表明古人类在该遗址活动的时间为距今5~6.5万年。本文对乌兰木伦遗址1号地点2010-2012年发掘出土的11934件石制品进行拼合研究,辨别出28个拼合组,含64件石制品,拼合率约为0.53%;其拼合形式可分为拼对(conjoin)和拼接(join)两种类型。石制品拼合组空间分布等相关信息显示,乌兰木伦旧石器时代中期遗址为原地埋藏。     

天津蓟县东营坊遗址出土的石制品

东营坊遗址位于天津蓟县境内。2007年,对该遗址进行抢救性发掘,发掘面积200m2,出土石制品90件。石制品原料系就地取材于遗址附近的河床或基岩;石制品类型包括石核、石片、石器和断块;剥片采用锤击法;石器以小型为主,包括刮削器和雕刻器,由硬锤直接加工而成,方向以正向居多。根据文化层底部哺乳动物化石的测年结果,遗址年代为旧石器时代中晚期过渡阶段或旧石器时代晚期早段。     

山东日照黄泥梁遗址2012年调查与试掘

2012年在日照黄泥梁遗址的调查、试掘工作中共获得石制品75件,其中试掘出土者71件,地表采集者4件。试掘石制品埋藏于黄土地层的古土壤层中,石制品个体较小,原料以闪长玢岩和脉石英为主,类型包括石核、石片、石器、断块等,属石片石器工业组合;地表石制品原料主要为燧石,类型有细石核、细石叶、端刮器等,属细石器工业组合。本次的调查、试掘确认了黄泥梁遗址的原地埋藏属性,肯定了遗址的发掘潜力和研究价值。黄泥梁遗址的发现和研究将推动山东旧石器考古研究的进展。     

吉林桦甸仙人洞旧石器遗址1993年发掘报告

1993年5—6月,仙人洞旧石器遗址发掘获得石制品197件,打制骨器18件,磨制骨器1件以及大量的动物骨骼化石。遗址分为上、下两个文化层,时代属旧石器时代早期之末或中期至晚期。石制品具有以小石制品为主的中国北方主工业的特点。     

Multiple outputation: inference for complex clustered data by averaging analyses from independent data

This article applies a simple method for settings where one has clustered data, but statistical methods are only available for independent data. We assume the statistical method provides us with a normally distributed estimate, theta, and an estimate of its variance sigma. We randomly select a data point from each cluster and apply our statistical method to this independent data. We repeat this multiple times, and use the average of the associated theta's as our estimate. An estimate of the variance is given by the average of the sigma2's minus the sample variance of the theta's. We call this procedure multiple outputation, as all "excess" data within each cluster is thrown out multiple times. Hoffman, Sen, and Weinberg (2001, Biometrika 88, 1121-1134) introduced this approach for generalized linear models when the cluster size is related to outcome. In this article, we demonstrate the broad applicability of the approach. Applications to angular data, p-values, vector parameters, Bayesian inference, genetics data, and random cluster sizes are discussed. In addition, asymptotic normality of estimates based on all possible outputations, as well as a finite number of outputations, is proven given weak conditions. Multiple outputation provides a simple and broadly applicable method for analyzing clustered data. It is especially suited to settings where methods for clustered data are impractical, but can also be applied generally as a quick and simple tool.     

Using Inverse Probability Bootstrap Sampling to Eliminate Sample Induced Bias in Model Based Analysis of Unequal Probability Samples

In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the sampling design phase. Questions may arise requiring the use of model based statistical tools such as multiple regression, quantile regression, or regression tree analysis. However, such model based tools may require, for ensuring unbiased estimation, data from simple random samples, which can be problematic when analyzing data from unequal probability designs. Despite numerous method specific tools available to properly account for sampling design, too often in the analysis of ecological data, sample design is ignored and consequences are not properly considered. We demonstrate here that violation of this assumption can lead to biased parameter estimates in ecological research. In addition, to the set of tools available for researchers to properly account for sampling design in model based analysis, we introduce inverse probability bootstrapping (IPB). Inverse probability bootstrapping is an easily implemented method for obtaining equal probability re-samples from a probability sample, from which unbiased model based estimates can be made. We demonstrate the potential for bias in model-based analyses that ignore sample inclusion probabilities, and the effectiveness of IPB sampling in eliminating this bias, using both simulated and actual ecological data. For illustration, we considered three model based analysis tools—linear regression, quantile regression, and boosted regression tree analysis. In all models, using both simulated and actual ecological data, we found inferences to be biased, sometimes severely, when sample inclusion probabilities were ignored, while IPB sampling effectively produced unbiased parameter estimates.     

Fitting distributions to microbial contamination data collected with an unequal probability sampling design

Aims

The fitting of statistical distributions to microbial sampling data is a common application in quantitative microbiology and risk assessment applications. An underlying assumption of most fitting techniques is that data are collected with simple random sampling, which is often times not the case. This study develops a weighted maximum likelihood estimation framework that is appropriate for microbiological samples that are collected with unequal probabilities of selection.

Methods and Results

A weighted maximum likelihood estimation framework is proposed for microbiological samples that are collected with unequal probabilities of selection. Two examples, based on the collection of food samples during processing, are provided to demonstrate the method and highlight the magnitude of biases in the maximum likelihood estimator when data are inappropriately treated as a simple random sample.

Conclusions

Failure to properly weight samples to account for how data are collected can introduce substantial biases into inferences drawn from the data.

Significance and Impact of the Study

The proposed methodology will reduce or eliminate an important source of bias in inferences drawn from the analysis of microbial data. This will also make comparisons between studies and the combination of results from different studies more reliable, which is important for risk assessment applications.     

乌兰布和沙漠天然白刺适应性群团抽样技术的研究

以乌兰布和沙漠地区天然白刺为具体研究对象,对不同抽样方法获取的数据进行了分析.研究结果表明,当适应性标准大于等于占位单元平均数时,适应性群团抽样比简单随机抽样更有效,并且可以保证估计精度;初始抽样比例越大,总体均值估计值和抽样相对效率越稳定.在该研究区域采用二阶适应性群团抽样,可以达到估计精度,但与简单随机抽样和适应性群团抽样相比效率偏低.     

Sampling design in large-scale vegetation studies: Do not sacrifice ecological thinking to statistical purism!

Most of the historical phytosociological data on vegetation composition have been sampled preferentially and thus belong to those ecological data that do not fulfill the statistical assumption of independence of observations, necessary for valid statistical testing and inference. Nevertheless, phytosociological data have been recently used for various ecological meta-analyses, especially in studies of large-scale vegetation patterns. For this reason, we focus on the comparison of preferential sampling with other sampling designs that have been recommended as more convenient alternatives from the point of view of statistical theory. We discuss that while simple random sampling, systematic sampling and stratified random sampling better meet some of the statistical assumptions, preferential sampling yields data sets that cover a broader range of vegetation variability. Moreover, today’s large phytosociological databases provide huge amounts of vegetation data with unrivalled geographic extent and density. We conclude that in the near future ecologists will not be able to replace the preferentially sampled phytosociological data in large-scale studies. At the same time, phytosociological databases have to be complemented with relevés of vegetation composed mostly of common and generalist species, which are under-represented in historical data. Stratified random sampling seems to be a suitable tool for doing this. Nevertheless, a methodology and input data for stratification have to be developed to make stratified random sampling an ecologically more relevant and practical method.     

Accounting for cluster sampling in constructing enumerative sequential sampling plans

Green's sequential sampling plan is widely used in applied entomology. Green's equation can be used to construct sampling stop charts, and a crop can then be surveyed using a simple random sampling (SRS) approach. In practice, however, crops are rarely surveyed according to SRS. Rather, some type of hierarchical design is usually used, such as cluster sampling, where sampling units form distinct groups. This article explains how to make adjustments to sampling plans that intend to use cluster sampling, a commonly used hierarchical design, rather than SRS. The methodologies are illustrated using diamondback moth, Plutella xylostella (L.), a pest of Brassica crops, as an example.     

Estimating the proportion of identifiable individuals and group sizes in photographic identification studies

Simple Bayesian statistical models are introduced to estimate the proportion of identifiable individuals and group sizes in photographic identification, or photo‐ID, studies of animals that are found in groups. The models require a simple random photographic sampling of animals, where the photographic captures are treated as sampling with replacement within each group. The total number of images, including those that cannot be identified, and the number of images that contain identifiable individuals are used to make inference about the proportion of identifiable individuals within each group and as the population when a number of groups are sampled. The numbers of images for individuals within each group are used to make inference about the group size. Based on analyses of simulated and real data, the models perform well with respect to accuracy and precision of posterior distributions of the parameters. Widths of posterior intervals were affected by the number of groups sampled, sampling duration, and the proportion of identifiable individuals in each group that was sampled. The structure of the models can accommodate covariates, which may affect photographic efficiency, defined in this study as the probability of photographically capturing individuals.     

Sampling Considerations for Disease Surveillance in Wildlife Populations

Abstract Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape-based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience-sample approach. Using chronic wasting disease in free-ranging deer (Odocoileus virginianus) as a simple illustration, we show that using probability sample designs with appropriate estimators provides unbiased surveillance parameter estimates but that the selection bias and coverage errors associated with convenience samples can lead to biased and misleading results. We also suggest practical alternatives to convenience samples that mix probability and convenience sampling. For example, a sample of land areas can be selected using a probability design that oversamples areas with larger animal populations, followed by harvesting of individual animals within sampled areas using a convenience sampling method.     

Validation of the orthogonal tilt reconstruction method with a biological test sample

Electron microscopy of frozen-hydrated samples (cryo-EM) can yield high resolution structures of macromolecular complexes by accurately determining the orientation of large numbers of experimental views of the sample relative to an existing 3D model. The "initial model problem", the challenge of obtaining these orientations ab initio, remains a major bottleneck in determining the structure of novel macromolecules, chiefly those lacking internal symmetry. We previously proposed a method for the generation of initial models--orthogonal tilt reconstruction (OTR)--that bypasses limitations inherent to the other two existing methods, random conical tilt (RCT) and angular reconstitution (AR). Here we present a validation of OTR with a biological test sample whose structure was previously solved by RCT: the complex between the yeast exosome and the subunit Rrp44. We show that, as originally demonstrated with synthetic data, OTR generates initial models that do not exhibit the "missing cone" artifacts associated with RCT and show an isotropic distribution of information when compared with the known structure. This eliminates the need for further user intervention to solve these artifacts and makes OTR ideal for automation and the analysis of heterogeneous samples. With the former in mind, we propose a set of simple quantitative criteria that can be used, in combination, to select from a large set of initial reconstructions a subset that can be used as reliable references for refinement to higher resolution.     

旧石器时代残留物分析:回顾与展望

残留物分析是1970年代在西方发展起来的一项功能学分析技术, 即借助自然科学的多种手段, 对工具表面加工对象残存物的提取和鉴定分析。本文对西方石制品残留物分析研究史做了回顾和综述, 简要介绍此种方法的概念、原理及相关技术问题, 同时对中国旧石器时代石制品残留物分析的前景进行了展望, 希望对相关理论和方法的引进和应用起到铺垫和促进作用。我国旧石器时代考古资源非常丰富, 残留物分析在石器功能解读、遗址环境重建、古人类行为复原和食谱分析等方面都会起到重要的作用, 成为破解很多学术疑点和难题的钥匙。