bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hierarchical
B
ayesian Space–Time Analysis
Authors: Mark Berliner L. 
Published: September 2006

Abstract
The hierarchical Bayesian (HB) approach to stochastic modeling and statistical analysis offers a framework for dealing with spatio‐temporal structures as well as the uncertainties prevalent in both our observations and scientific understanding of environmental processes. This article gives an introduction to the basics of HB space–time analysis with emphasis on modeling and prediction. Realistic space–time HB models lead to analyses requiring intensive computations. Until the advent of Markov Chain Monte Carlo (MCMC) as a device for Bayesian calculation, such computations were typically infeasible.
B
ayesian Methods and Modeling
Authors: Gelfand Alan E. 
Published: September 2006

Abstract

A recent article in
Science
magazine (286, 19 November 1999, pp. 1460–1464) noted the rejuvenation of the Bayesian inference paradigm and its increasingly warmer reception in the scientific community. Pertinent here, is its recent application to a wide range of environmetrics problems, allowing inferential capability that may not be available using classical methodology. This article provides a brief overview of Bayesian methodology and modeling beginning with Bayes' theorem. It then elaborates the Bayesian inference paradigm and the Bayesian modeling perspective. Historically, computation was the major impediment to the use of Bayesian analysis for complex models. Now, the wide availability of inexpensive, fast computing combined with breakthroughs in simulation‐based model fitting have removed this impediment. Bayesian models, which were formerly infeasible to examine, can now be fitted. More realistic models can be investigated.
Bioindicators
Authors: McGeoch Melodie A. 
Published: September 2006

Abstract
A biological indicator is a species (or group of species) that readily reflects some aspect of the state of the environment within which it is found. Bioindicators are used to detect or demonstrate the existence of a particular environmental state, and to monitor changes in that state. In this way they are used as tools to extract abiotic or biotic information from the environment so that this information can be used for making scientifically‐based management decisions.
Biomarkers
Authors: Thurston Sally W. 
Published: September 2006

Abstract
Recent advances in molecular biology have enabled investigators to measure a variety of biological indicators that can potentially help elucidate the causal pathway between environmental exposure and disease. Often called ‘biomarkers’, these can be defined as cellular, biochemical or molecular changes in a biological system such as human cells, tissues or fluids. Recent years have seen increasing interest in incorporating biomarkers into epidemiological research in general, but particularly in relation to cancer research. Such work generally falls under the rubric of molecular epidemiology. In the context of cancer, the contributions of molecular epidemiology include: (a) providing evidence that certain environmental agents act as carcinogens; (b) establishing evidence of a causal role between these agents and cancer; (c) identifying whether certain subpopulations are at an increased cancer risk when exposed to the agent; and (d) providing a basis for suggesting and developing strategies which can potentially reduce cancer risk.
Biomass
Authors: Parresol Bernard R. 
Published: September 2006

AbstractBiomass, the contraction for biological mass, is the amount of living material provided by a given area or volume of the earth's surface, whether terrestrial or aquatic. Biomass is important for commercial uses (e.g. fuel and fiber) and for national development planning, as well as for scientific studies of ecosystem productivity, energy and nutrient flows, and for assessing the contribution of changes in forestlands (especially tropical) to the global carbon cycle.
Biomonitoring
Authors: Conquest Loveday L. 
Published: September 2006

Abstract

According to
Webster's Dictionary
, the word
monitor
is related to the verb to warn, and includes the idea of observing a signal over time to see whether change is occurring. When the prefix
bio
is added to form the word biomonitoring, this encompasses the idea of tracking possible changes in a particular biological response, sometimes in correspondence to changes in human activity. In its common usage, the term biomonitoring refers to nonhuman biological systems, such as wildlife (e.g. birds, mammals), fish, and other aquatic organisms. For many of these organisms, the observations are recorded in the field (e.g. population counts, observations on breeding success), rather than in controlled laboratory experiments. The emphasis is that some living biological population is being observed (e.g. toxicants in bird populations, as opposed to toxicants from soil samples).
Bivariate Distributions
Authors: Christman Mary C. 
Published: September 2006

Abstract

Bivariate distributions play a special role in environmental science since they are the underpinnings of analyses of relationships between two random variables. These relationships include such analyses as biserial correlation between continuous and discrete random variables; measures of association in an
r
×
c
contingency table; Pearson's product–moment correlation; Kendall's tau; Spearman's rank correlation; and many others. In addition to studies of the dependency between two variables, interest may center on other analyses; for example, multivariate general (or generalized) linear modeling when two or more response variables are involved. Inferences involving two random variables rely on knowledge of their bivariate distribution. Probably the best known example is estimation and testing of the correlation coefficient in the bivariate normal distribution for two continuous random variables. There are many other distributions of importance as well, especially those relating two discrete random variables or a discrete to a continuous variable. This article describes a few of the more common distributions in environmental practice and, where appropriate, mentions the multivariate distribution of which the bivariate is a special case.
Bootstrap Resampling
Authors: Dixon Philip M. 
Published: September 2006

AbstractThe bootstrap is a resampling method for statistical inference. It is commonly used to estimate confidence intervals, but it can also be used to estimate bias and variance of an estimator or calibrate hypothesis tests. This article will illustrate bootstrap concepts using a simple example, describe different types of bootstraps and some of their theoretical and practical properties, discuss computation and other details, and indicate extensions that are especially appropriate for environmetric data. The methods will be illustrated using data on heavy metal concentrations in groundwater and magnesium concentration in blood.
Branch‐and‐Bound Algorithm
Authors: Whitaker David 
Published: September 2006

Abstract
The branch and bound algorithm is mostly used to solve global optimization problems with integer variables (unknowns), linear objective and linear constraints (integer linear programming (LP) problems). The recent increase in the speed of computation on desktop computers has meant that the algorithm can be applied to large problems with several hundred integer variables, which only a decade ago were intractable to any true optimization algorithms. Although a number of algorithms have been proposed for the integer LP problem, the branch and bound technique is used in almost all of the available computer software. This technique has proven to be reasonably efficient for practical problems because it has the embedded advantage of solving continuous linear programmes as subproblems. Furthermore, in some situations the embedded subproblem is a special case of a LP problem (such as assignment and transportation problems), which have more efficient algorithms than the general LP algorithm. Branch and bound methods are also easy to implement on parallel processors. An increasing number of applications are currently forthcoming in this area.
Capture–Recapture Sampling Designs
Authors: Lindberg Mark , Rexstad Eric 
Published: September 2006

Abstract
Reliable results from capture–recapture (CR) studies can only be ensured if diligent care is exercised prior to the acquisition of the first datum. We describe the considerations necessary for competent completion of CR studies. Rather than focusing on analytical issues, we discuss the stochastic processes that give rise to the data derived from such studies. Through this focus, we provide investigators with concepts they can influence to most closely adhere to the assumptions of analyses associated with CR studies. We hope to expose investigators to data collection issues associated with classic CR designs, and also to point out novel applications of these designs.
Carcinogenicity Model, Three‐State
Authors: Dinse Gregg E. 
Published: September 2006

Abstract
Early methods for analyzing tumor response data did not adjust for any of the following factors: (i) tumors generally are not observable in live animals, in which case all onset times are censored; (ii) much of this censoring is informative in the presence of tumor lethality and (iii) differential mortality exacerbates any biases that result from not properly adjusting for survival, censoring, and lethality. As experience was gained and the techniques for analyzing carcinogenicity data became more sophisticated, attention gradually focused on the tumor incidence function as the most appropriate endpoint. A multistate stochastic model provides a sensible statistical framework within which to make inferences about tumor incidence. At a minimum, the model must have two states, as incidence refers to the transition between a tumor‐free state and a tumor‐bearing state. Most models also include a third state to represent death. This article discusses the analysis of tumor incidence within the context of a three‐state model, concentrating on the first occurrence of occult, irreversible tumors at a single organ site.
Catch Statistics
Authors: Sampson D.B. 
Published: September 2006

AbstractFisheries science uses a wide variety of quantitative methods ranging from simple data tabulations to complex statistical models with hundreds of estimated parameters. Unlike the other sciences, however, in which experiments are often used successfully to evaluate competing hypotheses, many issues in fisheries are not amenable to controlled experiments because the fish are long‐lived and the fish populations are widely dispersed and suffer highly variable environmental conditions. Because fishers tend to operate in areas of high fish density where they can obtain favorable catch rates, in general the characteristics of the harvested fish will not be representative of the fish population at large. Further, different fish species often are not comparable in value and larger individuals of a given species are normally preferred to smaller ones. Hence, many of the quantitative methods in fisheries science are related to two broad problems: collecting data on the characteristics of the harvested fish and the magnitude of the harvest, and relating the harvest to the dynamics of the fish population in its entirety.
Catchment Hydrology
Authors: Letcher R.A. , Jakeman A.J. 
Published: September 2006

Abstract
The Ad Hoc Panel on Hydrology of the US Federal Council for Science and Technology recommended that hydrology be defined as ‘the science that treats of the waters of the Earth, their occurrence, circulation and distribution, their chemical and physical properties and their reaction with their environment, including their relation to living things’. More specifically catchment hydrology is the scientific study of that part of the hydrological cycle that relates to the concept of a catchment. This excludes the study of oceans or detailed atmospheric processes. A catchment can be defined as the area of land that drains to a point and may be subdivided into contributing subcatchments (or watersheds) by considering relative elevations and subsequently flow directions in different areas.
Change, Detecting
Authors: Esterby S.R. 
Published: September 2006

AbstractFundamental to the assessment of the effect of human activities on the environment is the detection of changes in environmental systems. It may be of interest to detect changes occurring over time, over space, or over both time and space. Although closely linked to the determination of status and the estimation of the size and components of change, detection is the primary consideration here. Methods for detecting change include: (1) graphical methods, exploratory and more formal; (2) tests of hypotheses, where the hypothesis characterizes the original status; and (3) diagnostic tests that detect departures from a specified model. Most methods considered in this article are for changes over a single dimension and will often have been developed for data collected over time. However, such methods will usually be applicable to data collected in one dimension over space. Datasets for which more than one dimension needs to be taken into account generally require more modeling and thus are less often investigated from the detection point of view.
Circular Data
Authors: Anderson‐Cook Christine M. , Otieno Bennett Sango 
Published: September 2006

AbstractDirectional data commonly occur in environmental studies when measurements are taken from an orientation or cyclical timing application. Circular data is the simplest case of this category of data, where the single response is not scalar, but angular or directional. Typical orientation observations are measured by a compass and indicate direction, while cyclical timing applications are measured by a clock or calendar. The key characteristic that differentiates circular data from data measured on a linear scale is its wrap‐around nature, with no maximum or minimum. Specific environmental endpoints that use directional measures include wind direction, plant or animal migration (orientation), as well as timing of meteorological or life cycle events during the day or in the calendar year (cyclical timing). Environmental applications include long‐axis orientations of fieldspar laths, cross‐bed azimuths of paleocurrents, and orientations of pebbles or rock cores. Also, the repeating nature of time increments, such as days, months or years, is captured by the wrapping‐around of the circle. Examples can include time of the daily maximum temperature, or occurrence of hurricanes during the year.
Coarse Woody Debris
Authors: Stahl Göran , Dolloff C. Andrew 
Published: September 2006

Abstract
Coarse woody debris (CWD), synonymous with large woody debris, is a term used to describe wood from dead trees. Although an exact definition is not available, many researchers include all pieces of wood that are at least 10 cm in diameter and 1m in length. CWD can consist of any coarse part of a dead tree, such as trunks or parts of trunks, branches, stumps, and root wads. A distinction is often made between standing dead trees, snags, and dead trees on the ground, logs. Dead roots in the soil are generally not considered as CWD although there is no principal reason why they should not be. Challenges to the measurement and quantification of CWD range from sampling issues to multivariate statistics for studying species occurrences and communities on dead wood. A brief overview of some specific environmetrical challenges is given in this article.
Species Competition
Authors: Young Linda J. 
Published: September 2006

Abstract
Interspecific competition arises when the interaction of two species has an adverse effect on both species. Competition may occur due to (a) exploitation (individuals from different species share the same limited resource(s)) or (b) interference (individuals from different species fight or exhibit other directly damaging behavior). Examples include two bird species seeking the same supply of insects, and tree seedlings of differing species sharing sunlight, water, and nutrients in the forest understory. Interest in competition dates at least to the time of Charles Darwin. Early studies tended to focus on discovering examples of one species replacing another. Competition studies have expanded to include investigations of the effect of environmental conditions on the competitive ability of one species relative to another, the role of competition in determining the distribution and abundance of organisms, and the conditions needed for species to coexist.
Compositional Data
Authors: Billheimer Dean 
Published: September 2006

AbstractCompositional data are vectors of proportions describing the relative contributions of each ofDcategories to the whole. This structure is an essential feature of data in many disciplines: in geology as oxide composition of rocks, in ecology as the relative abundance of plants or animals, in air pollution monitoring as the relative concentrations of different chemical species (among others). Statistical approaches for modeling compositional data exhibiting temporal or spatial correlation structure are described in this article. Because methods for compositions are not widely included in statistics education, a brief introduction is given to data characteristics and analysis techniques for independent observations. The focus is observations with three or more categories (D ≥ 3). With only two categories, compositional data are (essentially) univariate, and can be addressed by means of standard statistical techniques (e.g. logit transform). Since the compositional structure is present in a wide variety of problems, many of the methods summarized in this article have been developed outside the environmental context. The approach is to present methods in a general form, and note when there is special development for an environmental application.
Computer Modeling
Authors: Richter Otto , Seppelt Ralf , Söndgerath Dagmar 
Published: September 2006

AbstractThe starting point of computer modeling is the design of aconceptual model. The conceptual model aggregates our knowledge of the system and implies a thorough (albeit subjective) selection of components and processes judged essential for the processes under study in a given spatiotemporal context. A conceptual model is graphically presented in form of a compartment system. Compartments are defined with respect to morphology and to physical, chemical and biological states. A computer model implies the translation of a conceptual model into a code, i.e. an algorithm.
Constrained Optimization
Authors: Stein Alfred 
Published: September 2006

Abstract

Constrained optimization aims to find an optimum of a function in the presence of constraints. Constrained optimization generally yields a lower optimum on the optimization function as compared with unconstrained optimization, but it honors the presence of constraints. A simple example concerns maximization of the production of a farm subject to constraints on the amount of fertilizers (say
n
kg per week) and man hours (
h
hours per day). Another example is minimization of leaching towards the environment, subject to constraints on soil parameters, groundwater table and other environmental variables.
Control Theory
Authors: Young Peter C. 
Published: September 2006

Abstract
Control theory is concerned with the mathematical characterization and analysis of deterministic and stochastic dynamic systems for the purpose of identification, estimation and control. It emphasizes the importance of feedback processes, both as an integral aspect of all dynamic systems and as a means of artificially controlling or optimizing the behavior of such systems. In relation to environmetrics, the main importance of control theory relates to the impact that it has had on the analysis of environmental time series and the modeling of environmental systems. In addition, however, its powerful ability to improve our understanding of how dynamic systems behave in feedback terms can have a profound effect on how environmetric models are interpreted in physically meaningful terms.
Correlated Multinomial Data
Authors: Zhu Yiliang 
Published: September 2006

AbstractCorrelated multinomial data, of which correlated binomial data are a special case, often arise from studies with a clustered sampling plan. Grouped sampling and repeated measurements are common mechanisms of clustered sampling. Correlation induced by clustering must be accounted for in statistical analyses. Ignoring the correlation may lead to inaccurate or inefficient inference because of incorrect characterization of variation in the data. Sometimes, characterization of the correlation structure itself can be of interest, particularly in longitudinal studies.
Data Mining
Authors: Hand David 
Published: September 2006

AbstractData mining is a new discipline that has sprung up at the confluence of several other disciplines, stimulated chiefly by the growth of large databases. The basic motivating stimulus behind data mining is that these large databases contain information that is of value to the database owners, but this information is concealed within the mass of uninteresting data and has to be discovered. That is, one is seeking surprising, novel or unexpected information, and the aim is to extract this information. This means that the subject is closely allied to exploratory data analysis. However, issues arising from the sizes of the databases, as well as ideas and tools imported from other areas, mean that there is more to data mining than merely exploratory data analysis.
Developmental Toxicity Study
Authors: Regan Meredith M. 
Published: September 2006

Abstract
Development toxicology investigates the impacts on human development of exposure to potentially toxic substances or conditions. Controlled development toxicity studies in laboratory animals play an important role in the toxicological assessment of foods, drugs and chemicals. In contrast to carcinogenicity studies, methods to extrapolate safe exposure levels from animals to humans are not well developed. The mechanisms of most development toxicants are unknown, and statistical methods for carcinogenicity do not directly apply since the outcomes and the experimental design differ. Manifestations of development toxicity include death, malformations, growth alterations, and functional impairment. Improving methods of quantitative assessment of adverse development effects is a current research focus.
Discriminant Analysis
Authors: Seheult Allan 
Published: September 2006

Abstract

Suppose that two ‘populations’ (classes or groups) ∏
1
and ∏
2
of ‘individuals’ can be mainly distinguished by a ‘feature vector’
y
= (
y
1
, …,
y
p
) of
p
measurements. In discriminant analysis, the problem is to devise a rule which allocates (classifies) an individual with a given feature vector to one of the two populations, assuming it comes from one of them.
Disease Clustering
Authors: Elliott Paul 
Published: September 2006

Abstract

In recent years there has been heightened awareness about the possible effects of environmental pollution on health, reflecting an increasing public expectation of a clean and healthy environment. The public and media alike have focused on the possible risks to individuals and their communities from local sources of industrial pollution. These concerns have to some extent been fueled by scientific uncertainties about the health effects of low levels of pollution. All these issues tend to come to the fore when disease excesses (
clusters
) in the local community are reported or suspected. This article reviews some of the issues affecting the analysis and interpretation of clusters of disease, including examples where evidence of local disease excess is derived from a disease mapping exercise or a point source investigation.