An integrative and multi-indicator approach for wildlife health applied to an endangered caribou herd
Bibliographic record
Abstract
Assessing wildlife health in remote regions requires a multi-faceted approach, which commonly involves convenient samplings and the need of identifying and targeting relevant and informative indicators. We applied a novel wildlife health framework and critically assessed the value of different indicators for understanding the health status and trends of an endangered tundra caribou population. Samples and data from the Dolphin and Union caribou herd were obtained between 2015 and 2021, from community-based surveillance programs and from captured animals. We documented and categorized indicators into health determinants (infectious diseases and trace elements), processes (cortisol, pathology), and health outcomes (pregnancy and body condition). During a recent period of steep population decline, our results indicated a relatively good body condition and pregnancy rates, and decreasing levels of stress, along with a low adult cow survival. We detected multiple factors as potential contributors to the reduced survival, including Brucella suis biovar 4, Erysipelothrix rhusiopathiae and lower hair trace minerals. These results remark the need of targeted studies to improve detection and investigations on caribou mortalities. We also identified differences in health indicators between captured and hunter sampled caribou, highlighting the importance of accounting for sampling biases. This integrative approach that drew on multiple data sources has provided unprecedented knowledge on the health in this herd and highlights the value of documenting individual animal health to understand causes of wildlife declines.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".