Bibliographic record
Abstract
The unprecedented threats to the health and sustainability of wildlife populations are inspiring conversations on the need to change the way knowledge is generated, valued, and used to promote action to protect wildlife health. Wildlife Health 2.0 symbolizes the need to investigate how to improve connections between research expertise and policy or practices to protect wildlife health. Two imperatives drive this evolution: 1) growing frustrations that research is inadequately being used to inform management decisions and 2) the realization that scientific certainty is context specific for complex socioecologic issues, such as wildlife health. Failure to appreciate the unpredictability of complex systems or to incorporate ethical and cultural dimensions of decisions has limited the contribution of research to decision making. Wildlife health can draw from scholarship in other fields, such as public health and conservation, to bridge the knowledge-to-action gap. Efforts to integrate science into decisions are more likely to be effective when they enhance relevance, credibility, and legitimacy of information for people who will make or be affected by management decisions. A Wildlife Health 2.0 agenda is not a rejection of the current research paradigm but rather a call to expand our areas of inquiry to ensure that the additional contextual understanding is generated to help decision makers make good choices.
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 | 0.155 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".