Data limited assessment of selected North American anadromous charr stocks
Bibliographic record
Abstract
Charr populations are particularly susceptible to change, either from the application of harvest or environmental conditions such as climate variation. As an alternative to conventional fishery analysis, we analyze the sustainability and viability of selected North American charr stocks using a number of approaches. We compare several methods for data limited situations to determine the allowable harvest of the Ekalluk River, Paliryauk River, Halovik River, Jayco Lake and Lauchlan River Arctic Charr including Cadima’s Maximum Sustainable Production Method, Hierarchical Bayesian Surplus Production Models, a Status Quo Total Allowable Harvest method, Long-term Average Catch methods and the Depletion-Corrected Average Catch method. Each method provided a similar outcome in terms of the relative importance of stocks to the fishery. The predicted sustainable harvest of larger stocks such as the Ekalluk and Jayco varied more according to the method applied than the other stocks studied. While not a replacement for more comprehensive fishery models these methods can be useful in data poor situations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".