Towards a protocol for community monitoring of caribou body condition
Bibliographic record
Abstract
Effective ecological monitoring is central to the sustainability of subsistence resources of indigenous communities. For caribou, Arctic indigenous people's most important terrestrial subsistence resource, body condition is a useful measure because it integrates many ecological factors that influence caribou productivity and is recognized by biologists and hunters as meaningful. We draw on experience working with indigenous communities to develop a body condition monitoring protocol for harvested animals. Local indigenous knowledge provides a broad set of caribou health indicators and explanations of how environmental conditions may affect body condition. Scientific research on caribou body condition provides a basis to develop a simple dichotomous key that includes back fat, intestinal fat, kidney fat and marrow¬fat, as measures of body fat, which in autumn to early winter correlates with the likelihood of pregnancy. The dichotomous key was formulated on "expert knowledge" and validated against field estimates of body composition. We compare local indigenous knowledge indicators with hunter documented data based on the dichotomous key. The potential con¬tribution of community body condition monitoring can be realized through the continued comparative analysis of datasets. Better communication among hunters and scientists, and refinement of data collection and analysis methods are recommended. Results suggest that specific local knowledge may become generalized and integrated between regions if the dichotomous key is used as a generalized (semi-quantitative) index and complemented with other science and community-based assessments.
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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 | 0.143 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".