A multimodel approach to assess sustainable harvest levels for anadromous Arctic Char: challenges and implications for eco‐socially feasible long‐term comanagement tools
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Quantitative fish stock assessment requires accurate estimates of stock status, sustainable harvestlevels (SHLs) and inherent uncertainties to provide scientifically sound science advice on fisheriesmanagement decisions. The appropriateness and effectiveness of these estimates largely depend onthe quality and integrity of the temporal observations. In Canadian Arctic, community‐basedmonitoring initiatives have played significant roles in monitoring the stock status of exploitedresources for commercial, recreational and aboriginal fisheries. Bringing the multiple sets ofobservations on anadromous Arctic Char in Hornaday River systems during 1990‐2013, we in thisstudy developed a multi‐model statistical framework to assess the population dynamics and SHLs,incorporated with data‐limited model of depletion‐based stock reduction analysis (DB‐SRA), anddata‐rich surplus production model (SPM) and statistical catch‐at‐age model (SCA). In comparisonwith data inputs and model outputs, weighting by inverse variance (WIV) has been adopted toaccount for the effects of uncertainty sources on the model estimates. The modelling results indicatethe Arctic Char stock status is healthy, given the fact that current fisheries harvest levels are belowMSY.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".