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331.
Incorporating Collateral Data in Conservation Biology 总被引:1,自引:0,他引:1
NICHOLAS A. LINACRE§ ALLAN STEWART-OATEN† MARK A. BURGMAN PETER K. ADES‡ 《Conservation biology》2004,18(3):768-774
Abstract: Conservation biologists often need to set ecological modeling assumptions or estimate parameters from sparse data. In some cases this problem can be addressed by incorporating data from closely related species or from the same species at different sites (i.e., collateral data). Currently no structured methods exist for incorporating such information. An analogous problem in Actuarial science is to set premium rates in situations with little direct data on claim frequency or size. The rates are estimated using actuarial credibility theory, which incorporates collateral data with the direct data. actuarial credibility theory combined with the actuarial control cycle financial management process also provides an adaptive mechanism for updating assumptions. This theory may have some utility for ecologists wanting to incorporate collateral data in an adaptive management framework, a companion to approaches such as Bayesian updating. We describe the historical development of actuarial credibility theory from early ad hoc methods to empirical Bayes approaches. We explore some of the theory's strengths, such as relative simple formulae for incorporation collateral data, and we explore some of the theory's weaknesses, such as the use of the best linear approximation to the Bayes estimate. We illustrate potential applications of the theory using an example on the mortality rate of the Powerful Owl ( Ninox strenua ). 相似文献
332.
Walter W. Piegorsch 《Environmental and Ecological Statistics》1994,1(2):153-162
Hierarchical models are considered for estimating the probability of agreement between two outcomes or endpoints from an environmental
toxicity experiment. Emphasis is placed on generalized regression models, under which the prior mean is related to a linear
combination of explanatory variables via a monotone function. This function defines the scale over which the systematic effects
are modelled as additive. Specific illustration is provided for the logistic link function. The hierarchical model employs
a conjugate beta prior that leads to parametric empirical Bayes estimators of the individual agreement parameters. An example
from environmental carcinogenesis illustrates the methods, with motivation derived from estimation of the concordance between
two species carcinogenicity outcomes. Based on a large database of carcinogenicity studies, the inter-species concordance
is seen to be reasonably informative, i.e. in the range 67–84%. Stratification into pertinent potency-related sub-groups via
the logistic model is seen to improve concordance estimation: for environmental stimuli at the extremes of the potency spectrum,
concordance can reach well above 90%. 相似文献
333.
彭孟良 《湖南环境生物职业技术学院学报》2002,8(1):44-48
本文主要讨论基于Client/Server数据完整性约束及其如何实施企业业务规则,并以SQLServer和PowerBuilder为例,介绍了数据完整性约束的实现技术. 相似文献
334.
Christel Faes Marc Aerts Helena Geys Geert Molenberghs Lieven Declerck 《Environmental and Ecological Statistics》2004,11(3):305-322
Developmental toxicity studies are widely used to investigate the potential risk of environmental hazards. In dose–response experiments, subjects are randomly allocated to groups receiving various dose levels. Tests for trend are then often applied to assess possible dose effects. Recent techniques for risk assessment in this area are based on fitting dose–response models. The complexity of such studies implies a number of non-trivial challenges for model development and the construction of dose-related trend tests, including the hierarchical structure of the data, litter effects inducing extra variation, the functional form of the dose–response curve, the adverse event at dam or at fetus level, the inference paradigm, etc. The purpose of this paper is to propose a Bayesian trend test based on a non-linear power model for the dose effect and using an appropriate model for clustered binary data. Our work is motivated by the analysis of developmental toxicity studies, in which the offspring of exposed and control rodents are examined for defects. Simulations show the performance of the method over a number of samples generated under typical experimental conditions. 相似文献
335.
B. Sango Otieno Christine M. Anderson-Cook 《Environmental and Ecological Statistics》2006,13(3):311-324
Circular or angular variables indicating direction or cyclical time can be of great interest to scientists studying ecology,
biology or environmental issues. A common problem of interest in circular data is estimating a preferred direction and its
corresponding distribution. This problem is complicated by the so-called “wrap-around effect” on the circle, which exists
because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional
data, as they do not account for its circular nature. Three choices for summarizing the preferred direction (the sample circular
mean, sample circular median and a circular analog of the Hodges–Lehmann estimator) are discussed, with examples from environmental
and ecological applications. Similar to the linear data case, the relative emphases of different methods sometimes yield different
measures of preferred direction in the presence of outliers or lack of symmetry in the original data.
Received: November 2003 / Revised: June 2004 相似文献
336.
Gerda Claeskens Marc Aerts Geert Molenberghs Louise Ryan 《Environmental and Ecological Statistics》2002,9(4):357-377
We investigate several methods commonly used to obtain a benchmark dose and show that those based on full likelihood or profile likelihood methods might have severe shortcomings. We propose two new profile likelihood-based approaches which overcome these problems. Another contribution is the extension of the benchmark dose determination to non full likelihood models, such as quasi-likelihood, generalized estimating equations, which are widely used in settings such as developmental toxicity where clustered data are encountered. This widening of the scope of application is possible by the use of (robust) score statistics. Benchmark dose methods are applied to a data set from a developmental toxicity study. 相似文献
337.
Haolan Lu Cavan S. Reilly Sudipto Banerjee Bradley P. Carlin 《Environmental and Ecological Statistics》2007,14(4):433-452
Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables.
For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285,
2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries
computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic
alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure
using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution
of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require
several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli
distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments
(e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary).
We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation
to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods.
We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection
data set collected at the county level in the state of Minnesota. 相似文献
338.
On thresholds and environmental curve tensiometers 总被引:1,自引:0,他引:1
This paper considers distinctions between lognormal and mixture models. Emphasis is placed on two component mixtures where the lower valued subpopulation has a large mixing parameter. The density of this sort of mixture can be easily mistaken for a lognormal density. In order to compare such a mixture to a lognormal it is demonstrated that Galton's two parameter logmodel and Pearson's five parameternormal mixture are special, or limiting, cases of the same general mixture model. Consideration is given to the lognormal threshold parameter in order to devise a tool that can help distinguish mixtures from lognormals. Based on the threshold parameter, piloted procedures can help measure whether or not a curve is friable, in the sense that a brittle curve is better represented as a mixture than as a skewed lognormal. It is also shown that generalizations of Galton's product risk model can be represented interms of the threshold parameter Based on a tool called a curve tensiometer was designed to be applied as a graphical friability check in the ecological context of Fisher's classic Iris data and in the environmental context of a Santa Monica Bay fish consumption study. 相似文献
339.
C. Radhakrishna Rao 《Environmental and Ecological Statistics》1994,1(1):7-19
Statistical methods as developed and used in decision making and scientific research are of recent origin. The logical foundations of statistics are still under discussion and some care is needed in applying the existing methodology and interpreting results. Some pitfalls in statistical data analysis are discussed and the importance of cross examination of data (or exploratory data analysis) before using specific statistical techniques are emphasized. Comments are made on the treatment of outliers, choice of stochastic models, use of multivariate techniques and the choice of software (expert systems) in statistical analysis. The need for developing new methodology with particular relevance to environmental research and policy is stressed.Dr Rao is Eberly Professor of Statistics and Director of the Penn State Center for Multivariate Analysis. He has received PhD and ScD degrees from Cambridge University, and has been awarded numerous honorary doctorates from universities around the world. He is a Fellow of Royal Society, UK; Fellow of Indian National Science Academy; Foreign Honorary Member of American Academy of Arts and Science; Life Fellow of King's College, Cambridge; and Founder Fellow of the Third World Academy of Sciences. He is Honorary Fellow and President of International Statistical Institute, Biometric Society and elected Fellow of the Institute of Mathematical Statistics. He has made outstanding contributions to virtually all important topics of theoretical and applied statistics, and many results bear his name. He has been Editor of Sankhya and theJournal of Multivariate Analysis, and serves on international advisory boards of several professional journals, includingEnvironmetrics and theJournal of Environmental Statistics. This paper is based on the keynote address to the Seventh Annual Conference on Statistics of the United States Environmental Protection Agency. 相似文献
340.