A DETERMINANTS OF HEALTH CONCEPTUAL MODEL FOR FISH AND WILDLIFE HEALTH
Bibliographic record
Abstract
Our objectives were to establish if the determinant of health model used in the fields of human population and public health could be adapted to wildlife health; if it was applicable to more than one species; and if it reflected how fish and wildlife managers conceptualized health in practice. A conceptual model was developed using a scoping review on fish and wildlife health and resilience coupled with a participatory process with experts on barren ground caribou ( Rangifer tarandus groenlandicus) and sockeye salmon ( Oncorhynchus nerka) health. Both the literature and experts supported the concept of wildlife health as a cumulative effect involving multiple factors that extend beyond the disease and pathogen focus of many wildlife health studies and legislation. Six themes were associated with fish and wildlife health: 1) the biologic endowment of the individual and population; 2) the animal's social environment; 3) the quality and abundance of the animal's needs for daily living; 4) the abiotic environment in which the animal lives; 5) sources of direct mortality; and 6) changing human expectations. These themes were shared between salmon and caribou and conformed to expert perceptions of health. Determinants of health used in human public health are used for planning, development of policy, and guiding of research. The model we produced may also have use as a wildlife health planning tool to help managers identify health protection priorities and to promote actions across the determinants of health.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".