Petrarch’s Principle: how protected human‐made reefs can help the reconstruction of fisheries and marine ecosystems
Bibliographic record
Abstract
Petrarch’s Principle, named here, is to know things unseen, yet to ignore things seen. We apply the principle to the debate over the appropriate role and utility of human‐made reefs (HMRs) in fishery and ecosystem management by reviewing four linked issues that were discussed at the recent 7th International Conference on Artificial Reefs and Related Aquatic Habitats. First, deploying protected HMRs can help to mitigate the depletion of fishery stocks through overharvest and habitat degradation. Secondly, to achieve this objective effectively, it is essential that HMRs are protected as no‐take areas, and that, thirdly, HMRs are large, well‐planned, evaluated and monitored. Finally, ecosystem modelling and adaptive management responses are necessary to forecast and manage the benefits of HMRs. Moreover, uncertainty about the resolution of the well‐rehearsed attraction/production debate may be resolved by ensuring that HMRs are managed as protected no‐take areas. And to ensure an unbiased attitude that will aid the clarification of consequences, costs and benefits, we propose a change in terminology, from artificial reefs to human‐made reefs.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".