Comment on “Differences in predicted catch composition between two widely used catch equation formulations”Appears in Can. J. Fish. Aquat. Sci. <b>66</b>: 126–132.
Bibliographic record
Abstract
Branch (2009. Can. J. Fish. Aquat. Sci. 66: 126–132) described the two most common catch equation formulations in stock assessment models: the continuous (Baranov) one, which represents fishing mortality as an instantaneous rate, F, and the discrete one, in which it is represented as an exploitation rate, u. He claimed that the continuous formulation is preferable at high fishing mortality where a fish could encounter multiple sets of gear within a year. This claim is wrong for two reasons. First, it is based on the false supposition that the discrete catch equations require the assumption that fish encounter at most only one set of fishing gear in a year. Second, it is not possible to determine, for a specific stock assessment, whether one formulation is preferable to another solely on the basis of information about the fishery. The appropriate way to make this decision is to see which fits the data better.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.034 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".