A three‐pronged approach that leans on Indigenous knowledge for northern fish monitoring and conservation
Bibliographic record
Abstract
Investigating whether changes within fish populations may result from harvesting requires a comprehensive approach, especially in more data-sparse northern regions. Our study took a three-pronged approach to investigate walleye population change by combining Indigenous knowledge (IK), phenotypic traits, and genomics. We thank Larson et al. (2020) for their critiques of our study; certainly, there are aspects of their critique that are warranted and merit further investigation. However, we argue that their critique is over-stated and misleading, primarily given that (a) one of three prongs of our research, IK, was dismissed in their assessment of our study's conclusions; (b) our Bayesian size-at-age modeling should help to mitigate sample size issues; (c) their re-analysis of our size-at-age data does not actually refute our results; (d) genomic changes that we observed are nascent; (e) the data file that Larson et al. (2020) used for their genomic re-analysis was not correct; and (f) criteria that Larson et al. (2020) use for their genomic re-analysis were not properly justified.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".