Bibliographic record
Abstract
Destruction of habitat due to urban sprawl, pollution, and deforestation has caused population declines or even extinction of many of the world's approximately 2,600 snake species. Furthermore, misconceptions about snakes have made them among the most persecuted of all animals, despite the fact that less than a quarter of all species are venomous and most species are beneficial because they control rodent pests. It has become increasingly urgent, therefore, to develop viable conservation strategies for snakes and to investigate their importance as monitors of ecosystem health and indicators of habitat sustainability. In the first book on snakes written with a focus on conservation, editors Stephen J. Mullin and Richard A. Seigel bring together leading herpetologists to review and synthesize the ecology, conservation, and management of snakes worldwide. These experts report on advances in current research and summarize the primary literature, presenting the most important concepts and techniques in snake ecology and conservation. The common thread of conservation unites the twelve chapters, each of which addresses a major subdiscipline within snake ecology. Applied topics such as methods and modeling and strategies such as captive rearing and translocation are also covered. Each chapter provides an essential framework and indicates specific directions for future research, making this a critical reference for anyone interested in vertebrate conservation generally or for anyone implementing conservation and management policies concerning snake populations. Contributors: Omar Attum, Indiana University Southeast; Steven J. Beaupre, University of Arkansas; Xavier Bonnet, Centre National de la Recherche Scientifique; Frank T. Burbrink, College of Staten Island-The City University of New York; Gordon M. Burghardt, University of Tennessee; Todd A. Castoe, University of Colorado; David Chiszar, University of Colorado; Michael E. Dorcas, Davidson College; Lara E. Douglas, University of Arkansas; Christopher L. Jenkins, Project Orianne, Ltd.; Glenn Johnson, State University of New York at Potsdam; Michael Hutchins, The Wildlife Society; Richard B. King, Northern Illinois University; Bruce A. Kingsbury, Indiana University-Purdue University Fort Wayne; Thomas Madsen, University of Wollongong; Stephen J. Mullin, Eastern Illinois University; James B. Murphy, National Zoological Park; Charles R. Peterson, Idaho State University; Kent A. Prior, Parks Canada; Richard A. Seigel, Towson University; Richard Shine, University of Sydney; Kevin T. Shoemaker, College of Environmental Science and Forestry, State University of New York; Patrick J. Weatherhead, University of Illinois; John D. Willson, University of Georgia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".