Diverse knowledge systems reveal social–ecological dynamics that inform species conservation status
Bibliographic record
Abstract
Abstract Understanding changes over historical timescales is essential to gauge conservation status of a species. Modern ecological data typically neglect past magnitudes of change, which fortunately can be evaluated by bridging disparate knowledge sources. We synthesized zooarchaeological, historical, traditional, and western science knowledge to document changes in relative abundance of key species in Canada's northern abalone social–ecological system (SES) from the Holocene to present. Integrated models fit to traditional and western science data revealed 3.7% annual population decline from 1940s to 2010s for large abalone, although traditional knowledge density estimates were 9.5× higher than those derived from western science. Abalone are presently scarce compared to the mid‐1900s, but more abundant than before the early 1800s, calling their endangered status into question. Linking multiple knowledge sources can build SES understanding, facilitate power sharing, and support ecologically sustainable and socially just conservation outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".