Practical Approaches to the Conservation of Biological Diversity, eds. Richard K. Baydack, Henry Campa III, and Jonahan B. Haufler [Review]
Bibliographic record
Abstract
In 1994, in response to the growing need to address various questions relating to biodiversity conservation, the Biological Diversity Working Group was formed within the Wildlife Society.This book is an expansion and refinement of the papers presented at the Second Annual Conference of the Wildlife Society in Portland, Oregon, in 1995.In general, the main aim of the book is to present strategies for the conservation of terrestrial biological diversity.The target readers are managers and students.The contents are meant to help the former select an appropriate technique and the latter to compare and contrast methods as a learning experience in various courses.The 16 chapters of the book are divided into four parts.Part I is Conserving Biodiversity -Principles and Perspectives.Here we are introduced to the concept of biodiversity, its origin, why it is important, and current approaches to its conservation discussed at a general level.Part II consists of the strategies for conserving biodiversity.Each approach is described from theoretical perspective along with examples of practical case-study applications.Approaches are described in as many different ecosystems as possible in a wide range of locations.Part II is called Opportunities and Challenges and deals with common real-world constraints that trouble the enthusiasm for undertaking studies and management of biodiversity.Here the writing includes such chilling words and phrases as funding, fundamental, trivial, reliable knowledge, irrelevant, plethora of data, paucity of data, and shortage of time.Part IV is entitled Summary and Recommendations and attempts to outline a best course for the conservation of biodiversity and research needs for improved theory and management.The book concludes by proposing that it is possible and necessary to conserve biological biodiversity in virtually any management situation.Tell that to doctors fighting disease organisms or the forester spraying insecticide against Spruce Budworm.The book is well worth reading and we are indebted to the editors and authors for the variety of views assembled between the covers.There are 26 contributors ranging from graduate students to professionals.The writing is clear with few mathematical formulae.For the determined there are some 750 references and most are in the 1990s.A good index helps one into the thickets of the text and gain an overview of features of the landscape of the book.To someone familiar with the topic, other than checking what's new, I do not think they will gain a great deal from the book that they did not know or
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".