Reply to Andersen et al. (2016) “Assumptions behind size-based ecosystem models are realistic”
Bibliographic record
Abstract
Abstract In a recent publication (Froese et al., ICES Journal of Marine Science; 73: 1640–1650), we presented a critique of the balanced harvesting (BH) approach to fishing. A short section dealt with the size-spectrum models used to justify BH, wherein we pointed out the lack of realism of these models, which mostly represented ecosystems as consisting of a single cannibalistic species. Andersen et al. (ICES Journal of Marine Science; 73: 1651–1655) commented on our paper and suggested that we criticized size-spectrum models in general and that we supposedly made several erroneous statements. We stress that we only referred to the size-spectrum models that we cited, and we respond to each supposedly erroneous statement. We still believe that the size-spectrum models used to justify BH were highly unrealistic and not suitable for evaluating real-world fishing strategies. We agree with Andersen et al. that BH is unlikely to be a useful guiding principle for ecosystem-based fisheries management, for many reasons. The use of unrealistic models is one of them.
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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 | 0.021 | 0.103 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.038 | 0.083 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".