Reply to the comment by Francis 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
Francis (2010. Can. J. Fish. Aquat. Sci. 67: 763–765) writes a thoughtful response detailing concerns with my suggestion that the continuous (Baranov) catch formulation is preferable to the discrete catch formulation when fishing mortality is high (T.A. Branch. 2009. Can. J. Fish. Aquat. Sci. 66: 126–132). He suggests the discrete formulation allows for multiple gear encounters and that formulation choice should depend on which formulation better fits the data. Here I first distinguish between gear selectivity and availability and then show that our two views are complementary: the original assumes fish groups with differing gear selectivity but full availability, whereas Francis assumes fish groups fully selected by fishing gear but with differing availability. I maintain that the discrete formulation only models a single instantaneous interaction between fish and fishing gear and therefore only part of the population can be caught if fish groups have equal gear selectivity that is less than 100%, whereas under the same assumptions, the continuous formulation would allow the entire population to be caught. Finally, when the balance between gear selectivity and availability is unknown, I agree that formulation choice could be driven by model fits to the data, although formulation choice could also be based on how the fishery operates.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.037 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".