Reply to Holm <i>et al</i>. 2022, “Comment on ‘Five centuries of cod catches in eastern Canada,’ by Schijns <i>et al</i>.”
Bibliographic record
Abstract
In our original contribution, we used a new simple method (CMSY, Froese et al., 2017) and the time series of catches of Hutchings and Myers (1995) to analyse five centuries of cod catches in eastern Canada (Schijns et al., 2021). Holm et al. (2022) point out that there was actually an improved historical time series that we had overlooked. As we note in our paper, it is important to use the best available data, including all withdrawals in the case of catches, in order to realize the full potential of an exploited resource. Thus, we are in agreement with the points raised by Holm et al. (2022), and acknowledge that the more complete and accurate data they point out will improve the reliability of the assessment results. We welcome this opportunity to bring together interdisciplinary thinkers, such as fisheries scientists and historians. Therefore, we have agreed to collaborate on a joint paper where we will perform CMSY modelling on revised historical catch estimates to investigate what the consequences of these new catch levels are for stock and fisheries sustainability. In this effort, catches will be updated from 1500 to 1790 based on Holm et al. (2021), from 1815 to 1934 based on the Government of Newfoundland data (HistStats, 1970, Table K-7; Alexander, 1976), and will include new reconstructed estimates based on domestic consumption and the French exports from part of the area.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.051 | 0.067 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".