Science Response : Arctic Surfclam on Banquereau and Grand Bank
Bibliographic record
Abstract
An update on the status of the offshore Arctic Surfclam (Mactromeris polynyma) resource was requested by Maritimes Region Resource Management to support harvest level decisions in the Arctic Surfclam fishery. Surfclam is assessed on a multi-year assessment schedule, with Stock Status Updates produced in interim years. The basis for assessing Arctic Surfclam on Banquereau and Grand Bank was examined at a framework meeting on June 28–29, 2016. During the framework, a fisheries-dependent assessment methodology was developed for Banquereau using a spatially disaggregated surplus production model (Hubley and Heaslip 2018). This method was used to provide an assessment of the stock status on Banquereau and potential harvest levels based on two removal rates. This report updates fisheries information (landings, catch per unit effort, fishery footprint) and secondary indicators to the end of the 2023 fishing season for both Grand Bank and Banquereau, as well as biomass model results from Banquereau, and assesses them against agreed upon reference points and thresholds. These banks were last assessed in 2017 (DFO 2017, Hubley et al. 2020) with updates conducted annually from 2018 – 2023 (DFO 2023). This Science Response Report results from the regional peer review on September 12, 2024, on the Stock Status Update for Arctic Surfclam on Banquereau Bank and Grand Bank.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.015 |
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".