Summary of factors that affect survey and fishing catchability and data available regarding the NAFO Subarea 0+1 (offshore) Greenland Halibut (Reinhardtius hippoglossoides) stock and fishery
Bibliographic record
Abstract
Fishery-independent stock assessment surveys rely on consistent sampling methods, equipment and strategies to minimize variability in the data that could mask or confound trends in the assessed stock. Surveys typically use the same vessel and fishing gear each year as changes in either can affect the catchability of species during the survey, and therefore, the representativeness of the survey data. An unrecognized decline or increase in catchability could be interpreted as a decline or increase, respectively, in stock status. These observations could then lead to ill-informed advice to reduce or increase the level of harvest, respectively, producing hidden economic or conservation consequences. Factors that affect catchability, particularly during trawling surveys, are summarized here to help inform data considerations when there are changes in the vessel or gear used to conduct surveys. Data pertaining to the Greenland Halibut (Reinhardtius hippoglossoides) stock in the Northwest Atlantic Fisheries Organization (NAFO) Subareas 0+1 (offshore) (GH-0+1) are collected during several fishery-independent surveys, as well as through commercial logbooks and at-sea observer records. Fisheries and Oceans Canada (DFO) and the Greenland Institute of Natural Resources (GINR) use data from groundfish-focused surveys conducted in Divisions 0A-South and 1CD and a shrimp and fish focused survey in Divisions 1A to F to assess the status of the GH-0+1 stock. However, additional data are available from the Canadian Northern Shrimp Research Foundation survey and a few less frequent surveys conducted by DFO and the Greenland Institute of Natural Resources (GINR) in Divisions 0A-North, 0B and 1AB. This document summarizes the methods used for each survey, the types of data collected and the years and areas where surveys occurred, to provide an overview of data available for assessment and modelling of the GH-0+1 stock.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".