Priorities for Improving the Scientific Foundation of Conservation Policy in North America
Bibliographic record
Abstract
The Society for Conservation Biology (SCB) can enhance conservation of biodiversity in North America by increasing its engagement in public policy. Toward this end, the North America Section of SCB is establishing partnerships with other professional organizations in order to speak more powerfully to decision makers and taking other actions-such as increasing interaction with chapters-geared to engage members more substantively in science-policy issues. Additionally, the section is developing a North American Biodiversity Blueprint, which spans the continental United States and Canada and is informed by natural and social science. This blueprint is intended to clarify the policy challenges for protecting continental biodiversity, to foster bilateral collaboration to resolve common problems, and to suggest rational alternative policies and practices that are more likely than current practices to sustain North America's natural heritage. Conservation scientists and practitioners can play a key role by drawing policy makers' attention to ultimate, as well as proximate, causes of biodiversity decline and to the ecological and economic consequences of not addressing these threats.
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 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 | 0.070 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 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".