bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dispersion in Confined Spaces
Authors: Phillips M. 
Published: September 2006

Abstract
The release of a gas or some other form of airborne substance into a confined space can present some form of hazard. Some gases are, for example, inherently toxic while others may result in hazardous conditions because they displace the oxygen necessary for normal respiration. In either case, an appreciation of the processes that result in the dispersion of the substance around the space is likely to be essential to a full understanding of the hazard present. Gases or airborne particles can be dispersed in confined spaces by a variety of physical processes. Which process or processes dominate in any particular situation depends on many factors. The most significant factors are normally the nature of the dispersing substance, the prevailing conditions in the space in which it disperses, and the geometry of the space. Since conditions such as air movements and ventilation rates are difficult to estimate or measure precisely and comprehensively, estimates made can rarely be relied on to provide highly precise data. Nevertheless, the techniques described in this article do provide valuable means of analyzing and quantifying the likely consequences of the release of gaseous or airborne substances in a confined space, albeit in broad terms rather than precise ones.
Dose‐Response Model
Authors: Gaylor David 
Published: September 2006

Abstract

A measurement,
Y
, of an effect can be represented by
Y
= E(
Y
) + error. E(
Y
) is the expected (true) value of an effect and error is the deviation of the measurement from its true value due to inherent measurement error and/or experimental variation. A dose–response model is a mathematical function
f
that relates the expected value, E(
Y
), of some measurable effect,
Y
, to a dose,
d
, of a substance E(
Y
) =
f
(
d
).
Dose–Response Modeling for Clustered Data
Authors: Chen James J. 
Published: September 2006

Abstract
Clustered data refer to a set of correlated responses. Clustered data arise naturally when the experimental units are subject to random variations, biologically or environmentally. For example, different animals represent different genetic values inherited from parents that may also be nested within herds or spatial entities. Clustered data also result from study designs, such as repeated observations on the same subject or sampling from correlated subsamples. A great deal of statistical research on dose response modeling of clustered data is devoted to animal reproductive and developmental toxicity studies. In developmental toxicity experiments, chemicals are administered to the dam (female animal), but the observations are made on individual fetuses. The clustering of fetuses within the litters induces the cluster (litter) effect. Modeling the clustered data should account for the correlation among the individual responses within a cluster to draw valid and efficient inferences. Two common approaches for fitting dose response models to clustered data are the likelihood‐based and generalized estimating equations (GEE) (or quasi‐likelihood) approaches.
Dynamic Programming
Authors: Whitaker David 
Published: September 2006

AbstractDynamic programming is a technique whose development is mainly due to Bellman. The term ‘dynamic programming’ originally arose because the technique was closely associated with computer programming where the solution to a problem was often obtained using the computer. A better view of the technique is to think of it as a method for deriving solutions tomultistage decision processes, which often, because of their computational intensive nature, rely on the use of the computer for solution. Dynamic programming is applicable to a wide variety of situations where the problem can be structured into stages and it is clear that the return from a procedure optimizing over each individual stage of the problem does not give the overall optimum. For example, when the stages are represented by months, a sacrifice in the return from a decision in January may enhance returns in future months so that the overall return at the end of the year is maximized. These multistage problems occur naturally in a wide variety of practical situations in economics, environmental science, science and engineering, ecology, and even sports.
Ecological Society of America
Authors: Porter John H. 
Published: September 2006

Abstract
The Ecological Society of America (ESA) is a 7600‐member scientific society that focuses on improving communication among ecological scientists and between ecologists and society in general. Membership in the society is open to anyone with an interest in the advancement of ecology. The society has as its goals to: (i) promote ecological science by improving communication among ecologists; (ii) raise the public's level of awareness of the importance of ecological science; (iii) increase the resources available for the conduct of ecological science; and (iv) ensure the appropriate use of ecological science in environmental decision making by enhancing communication between the ecological community and policy‐makers. The society achieves these goals through annual meetings, the publication of scientific journals and special publications, supporting regional chapters and topic‐based sections and a operating a public‐affairs office.
Time Series, Ecological
Authors: Solow A.R. 
Published: September 2006

Abstract

The most common situation in which time series data are the central object of ecological analysis is in population dynamics/population modeling. Let

X
t

be the size or density of a population in period
t
. A general deterministic model of the dynamics of this population is the first‐order nonlinear difference equation:

X
t

=

X
t−

1
F
(

X
t

−1
) where
F
is a non‐negative function.
Ecosystem Monitoring
Authors: Kimmins Hamish 
Published: September 2006

AbstractThe world's population has increased 6 times over the past 200 years. It has doubled over the past 40 years. Air and water pollution, climate change, loss of species, soil erosion and salinization, desertification, loss of forest, and a range of other undesirable environmental alterations have accompanied this unprecedented increase in human numbers. While the rate of population growth is declining, human pressure on the environment will continue to increase as human standards of living and per capita consumption increase, and it is clear that a fundamental change must occur in the relationship between humans and the global ecosystem if we are to avoid highly undesirable environmental change. Development of a sustainable relationship between humans and the environment requires an understanding of, and respect for, both human needs and desires, and the structure and functioning of the world's ecosystems. Policies and resource management practices must be developed that sustain desired ecosystem conditions and functions. To ensure that these policies and practices are effective, the condition of the world's ecosystems must be monitored.
Edge Effect
Authors: Stevens Jr Don L. 
Published: September 2006

AbstractA common objective in sampling a population of biological, ecological, or environmental concern is an inference about the mean or total of some attribute per unit area. In many instances, population attributes are measured in the field by compositing information collected on plots of non‐negligible extent. For example, a forest inventory may count the number of trees and measure their basal area on circular plots. Fish population size might be estimated by counting the number of fish in selected stream reaches in a basin. Estimates of total biomass in a region could be obtained by collecting all biomass in randomly located quadrats. Unfortunately, it is well documented that this general method can result in a discrepancy, often callededge‐effect bias, between the expected value of the estimator and the true value of the total. This discrepancy results from lack of coherence between the measured density and the true density stemming from injudicious handling of plots that are near to or actually straddle the domain boundary. Unless such plots are treated appropriately, population elements near the boundary are weighted incorrectly in computing the overall total. Because edges often have properties that are distinctly different from interior properties, giving an incorrect weight to the edges can result in substantial bias. This article gives an explicit definition of edge‐effect bias, provides a sufficient condition for its absence, and discusses techniques to counter it.
Electromagnetic Fields
Authors: Savitz David A. 
Published: September 2006

Abstract
Concerns with possible health effects of low‐level exposure to extremely low‐frequency electric and magnetic fields have been prominent for over 20 years. The hypothesis of particular interest is that prolonged exposure, over periods of months to years, to relatively elevated power‐frequency (50 or 60 Hz) magnetic fields in the environment increases the risk of leukemia and brain cancer. Beyond the substantial public interest and policy relevance to the question of whether this ubiquitous exposure poses a threat to human health, the topic serves as a prototype of a low‐level, ambiguous environmental health concern. Like the concerns with persistent organochlorines such as dichlorodiphenyltrichloroethane (DDT), polychlorinated biphenyls (PCBs), or dioxin, or with drinking water disinfection by‐products, human health effects are at the margins of being detectable if present at all. Furthermore, as with many ubiquitous exposures such as indoor Radon, it is difficult to clearly identify exposure gradients: a few individuals may be highly exposed but most of the population is in a range in which discrimination of exposure levels is a challenge. Finally, because the exposure is so widespread, even a modest influence on disease risk is of public health interest. That is, public health concerns push back the boundaries of what is scientifically feasible, creating tension between the knowledge that is desired for setting policy on regulatory standards, and the often rather blunt instruments of environmental epidemiology. In the face of this pressure, statistical methods for maximizing the sensitivity and precision of risk estimates are paramount.
Emission Inventory
Authors: Fujita Eric M. 
Published: September 2006

Abstract
An emission inventory is a comprehensive listing of air pollutant emissions within a specific geographic location and time interval. Emission inventories provide the technical foundation for air quality management plans designed to improve or maintain ambient air quality. Air pollution control agencies use emission inventories to identify potential control measures and sources that are subject to controls, to determine control program effectiveness and to predict future air quality through air quality simulation models.
Empirical Bayes Methods
Authors: Louis Thomas A. 
Published: September 2006

AbstractStatistical design, analysis, and reporting are fundamental to environmental research and assessment; to environmetrics. The variety and complexity of applications rapidly grows with a commensurate growth in statistical design and analysis. Environmental applications include all aspects of design, the analysis of spatially structured information (exposure, health outcomes, and demographics), nonlinear dose–response assays, interspecies extrapolation, placement of air or water monitoring stations, remote sensing, and gene–environment interactions. Analytic challenges include stabilizing estimates via statistical modeling and otherwise ‘borrowing information’, integrating information that is misaligned in space and time, dealing with missing data, accounting for measurement error in input information (e.g. exposures), missing data, causal modeling, addressing nonstandard goals and ensuring that statistical evaluations account for the dominant stochastic and modeling uncertainties. In addressing these and other challenges and goals, Bayes and empirical Bayes (EB) designs and analyses have proven valid, efficient, and informative. The beauty of the Bayesian approach is its ability to structure complicated assessments, guide development of appropriate statistical models and inferences, and produce summaries that properly account for uncertainties. Properly structured, these methods can produce objectively valid analyses, commonly more effective than traditional methods. When available, prior information can be integrated into the design and analysis. Computing innovations enable implementation of complex, relevant models and applications burgeon. This article outlines the Bayesian approach, traces the history of the EB approximation, gives a small sampling of environmetric applications and speculates on future developments.
Environmental Protection Agency,
US
Authors: Olsen Anthony R. 
Published: September 2006

Abstract
This article gives an overview of the U.S. Environmental Protection Agency for use in an Encyclopedia of Environmental Statistics. A perspective is given on the history of the Agency's formation, its mission, its legal authority, and its organizational structure. Selected activities of the Agency are given to provide a limited view of how the agency addresses environmental issues and cases in which environmental statistics have been used.
Sampling, Environmental
Authors: Stehman Stephen V. , Overton W. Scott 
Published: September 2006

AbstractEnvironmental sampling is the science of collecting and analyzing data to address questions in diverse fields, such as ecology, environmental engineering, environmental policy, fisheries, forestry, and wildlife science. The goal of sampling is to describe collective properties of populations defined as a universe. These collective properties are estimated using information from a subset of the universe called the sample. The universe may consist of discrete objects such as lakes, wetlands, stream segments, forest stands, plant or animal populations, environmental organizations, or recreational users, or it may be defined spatially, for example by delineating an area on a map. Environmental sampling objectives typically address questions of status and/or trend. Status refers to current values of population attributes. How many acidic lakes are there in the northeast US potentially sensitive to acidification and what are their characteristics? How many individuals of a rare plant population exist, and what is the geographical distribution of this population? What is the status of a nation’s wetlands? Trend focuses on how populations are changing over time. Are certain amphibian populations declining in South American forests? Is tree disease increasing following a damaging ice storm? How much agricultural land is being converted to residential and commercial use?
Environmental Tobacco Smoke (
ETS
)
Authors: Tweedie R.L. 
Published: September 2006

Abstract
Since the early 1980s, there has been an ongoing debate about the health effects of exposure to environmental tobacco smoke (ETS), also called passive smoke or second‐hand smoke. Prior to this period, most reviews had not found ETS to be associated with increased health risk. Indeed, in 1979 the US Surgeon General found no real indication that ETS was associated with either lung cancer or heart disease. In 1981, however, two studies were announced which identified an elevated relative risk of lung cancer in women with spouses who smoked. This has been followed by some 40 further studies, the results of which differ. A number show a significantly elevated risk, the great majority are rather equivocal, and a few are actually negative.
Epidemic Models
Authors: Renshaw Eric 
Published: September 2006

Abstract
The importance of studying disease outbreak and spread cannot be overstated. In fourteenth century Europe the Black Death killed 25 million people out of a population of 100 million; the Aztecs lost half their population of 3.5 million from smallpox; whilst around 20 million people died in the world pandemic of influenza in 1919. Today the overriding epidemic concern is the spread of human immunodeficiency virus (HIV) acquired immune deficiency syndrome (AIDS), to the considerable detriment of the vast numbers of people suffering from less mediaconscious diseases such as malaria, schistosomiasis, filariasis, and hookworm disease. Parallel problems in marine and agriculturally based environments take a similar toll on plant, animal, and fish populations. Human attempts to control such epidemiological disasters can themselves lead to further problems e.g. the improper use of pesticides and management strategies. Increasing understanding of the underlying processes involved is therefore one of the major environmental problems of our age, and the best way forward is through the careful use of mathematical modeling.
Escape Trajectory, Ecological
Authors: Domenici P. 
Published: September 2006

AbstractEscape responses consist of sudden accelerations as reactions to threatening stimuli and are present in most animals as a means of avoiding predation. Escape responses are currently receiving increased attention as valuable models for animal behavior, ecology, and neurobiology. The path along which an animal moves during an escape response (its escape trajectory) has attracted the interest of ecologists and behaviorists in relation to its functional role in predator–prey interactions. Escape trajectories (ETs) as considered in this article refer to the initial prey response to a predator’s attack. Such a trajectory should be measured at the end of the main rotational motion present during the escape response, which usually corresponds to a specific kinematic stage of the animal’s locomotion. Beyond this stage, prey may continue escaping along a zigzag path, especially if predators follow up their attack with a chase.
Exposure Assessment
Authors: Levy Jonathan 
Published: September 2006

Abstract
Human exposure to a contaminant has been defined as an event consisting of contact with a specific concentration of the contaminant at a boundary between the human and the environment (e.g. lung or skin) for a specific amount of time. Exposure therefore refers to the intensity, frequency, and duration of contact with a chemical, which is distinct from the actual amount of chemical absorbed or received by target organs (the dose). Exposure assessment is the process of quantifying and interpreting the pathway from the emission of a chemical to human exposure, which can have applications within risk assessment, environmental epidemiology, or pollution control development. The goal of exposure assessment is often to determine the distribution of exposures to a specific pollutant across individuals, which provides both the population mean exposure (relevant for quantifying population risks) and the exposure levels for more highly‐exposed or susceptible individuals.
Fisheries
Authors: Myers Ransom A. 
Published: September 2006

AbstractFisheries biologists use statistical models to transform disparate data, such as catch, research surveys estimates, and the catch rates of fishermen into estimates of abundance, management regulations, and risk analysis. The result is an integrated assessment of a fish stock – a model of population dynamics that can be used to evaluate management decision. The core statistical problem in fisheries is the estimation of parameters of such a model. Such a statistical model combines data from many sources, using a variety of likelihoods, each representing a different piece of the data.
Forest Fire Models
Authors: Preisler Haiganoush K. , Weise David R. 
Published: September 2006

AbstractForest fire models pertain to at least one of the three phases of fire management: prefire planning (fire risk models), fire suppression (fire behavior models), and postfire evaluation (fire effects and economics models). A model can be as simple as a formula calculating a fire danger index, or as complex as a numerical model that requires hours of computer time to arrive at an output.
Fuzzy Numbers and Nonprecise Data
Authors: Viertl Reinhard 
Published: September 2006

Abstract

Real data obtained from measurements of continuous quantities are not precise numbers or vectors but more or less nonprecise. This uncertainty is different from measurement errors and has to be described formally in order to obtain realistic results of data analysis. Real‐life examples are poisons in different environmental media at a fixed time. These are typically not a precise multiple of the scale unit for the measurements. Such data are called
nonprecise
. In the past, this kind of uncertainty was neglected in statistical analysis. The reason behind this was the philosophical idea of the existence of a ‘true’ value which is identified with a real number times the measurement unit; but this is not realistic. The formal description of such nonprecise measurements can be given using so‐called
fuzzy numbers.
Gaussian Process
Authors: Davis Richard A. 
Published: September 2006

Abstract

The class of Gaussian processes is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space, or time and space. The popularity of such processes stems primarily from two essential properties. First, a Gaussian process is completely determined by its mean and covariance functions. This property facilitates model fitting as only the first‐ and second‐order moments of the process require specification. Second, solving the prediction problem is relatively straightforward. The best predictor of a Gaussian process at an unobserved location is a linear function of the
observed
values and, in many cases, these functions can be computed rather quickly using recursive formulas.
Gene–Environment Interaction
Authors: Niu Tianhua 
Published: September 2006

Abstract
Gene–environment interaction refers to the fact that individuals with different genotypes have different responses to the same environmental factor. It represents the major component of genetic epidemiology, an emerging discipline that aims at elucidating the role of genetic factors as well as their interaction with environmental factors in the occurrence of disease in populations. There are two broad categories of approaches in examining gene–environment interactions: conventional and nonconventional study designs. Conventional study designs include mainly case–control and cohort studies in which gene–environment interaction can be assessed by incorporating an interaction term into the statistical model. Quite a few publications have addressed the issue of power and sample size calculations using such approaches. However, exposure misclassification can lead to bias in the estimation of an interaction effect, and an increased sample size may be needed in applying these strategies. Therefore, nonconventional study designs, which include case‐only studies, twin studies, transmission/disequilibrium tests (TDT), and affected relative pair studies, have become more and more popular. This article primarily focuses on nonconventional approaches.
Generalized Linear Mixed Models
Authors: McCulloch Charles E. 
Published: September 2006

AbstractGeneralized linear mixed models (GLMMs) are a class of models that incorporates random effects into the linear predictor of a generalized linear model (GLM). This allows the modeling of correlated data within the context of GLMs and greatly extends their breadth of applicability. They thus include both linear mixed models (LMMs) and GLMs as special cases.