A proposed lexicon of terms and concepts for human–bear management in North America
Bibliographic record
Abstract
We believe that communication within and among agency personnel in the United States and Canada about the successes and failures of their human–bear (Ursidae) management programs will increase the effectiveness of these programs and of bear research. To communicate more effectively, we suggest agencies clearly define terms and concepts used in human–bear management and use them in a consistent manner. We constructed a human–bear management lexicon of terms and concepts using a modified Delphi method to provide a resource that facilitates more effective communication among human–bear management agencies. Specifically, we defined 40 terms and concepts in human–bear management and suggest definitions based on discussions with 13 other professionals from the United States and Canada. Although new terms and concepts will emerge in the future and definitions will evolve as we learn more about bear behavior and ecology, our purpose is to suggest working definitions for terms and concepts to help guide human–bear management and research activities in North America. Applications or revisions of these definitions may be useful outside of North America.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".