Mapping and navigating mammalian conservation: from analysis to action
Bibliographic record
Abstract
Although mammals are often seen as important objects of human interest and affection, many are threatened with extinction. A range of efforts have been proposed and much work has been done to try to conserve mammals, but there is little overall understanding of what has worked and why. As a result, there is no global-scale, coordinated approach to conserving all mammals. Rather, conservation efforts are usually focused at jurisdictional levels where relevant legislation and policies are in force. To help build the framework for a global-scale approach, in this paper we review the many ways that have been proposed for conserving mammals. First, we examine the overall pattern of threat faced by mammals at the global level. Secondly, we look at the major structuring issues in prioritizing and planning mammal conservation, examining in particular the roles of values and scale and a set of approaches to conservation, each of which varies along a continuum. Finally, we lay out the steps necessary to move from planning to implementing mammalian conservation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".