A Simple Matrix Based Analysis Of Multiple Area Tagging Data Applied To The Newfoundland Northern Cod Stock
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.A simple model for the analysis of tagging data is developed. This accounts for both harvest within stock components and migration between components. An exact matrix solution is provided for the simple case where all components are tagged in a year and extensions of this are constructed to handle the singular matrix situations where not all data are available for all years. This model is applied to inshore components of the Newfoundland Northern (NAFO subdivisions 2J3KL) cod stock and to inshore and offshore components of the 3Ps cod stock. Assessment of this stock is complicated by post moritorium fisheries being conducted only on inshore sub populations, (Lilly et al 2001). These are not fully covered in the groundfish survey which provide the main link to the pre-moritorium population estimates. Hence, assessments of abundance of these sub-populations must rest heavily on the results of tagging experiments. Results indicate that the biomass of components of the Northern cod have remained at about 40Kt for the past three years and that harvests rates have typically been of the order of 10% on the major 3L North component and rather higher on the 3K component during this period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".