We should not assume that fishing jellyfish will solve our jellyfish problem
Bibliographic record
Abstract
Abstract Whether jellyfish are increasing or not in the global ocean is a subject of some debate, but the fact remains that when they bloom, jellyfish can negatively affect local economies. Despite this, there has been no robust debate about the idea of deliberately removing jellyfish as a means of population control. Here, we discuss the effects of fishing for jellyfish, either as a sustainable resource and/or as a way to simply reduce their nuisance value, on both individual jellyfish populations and the ecosystem. Given that the drivers influencing each local bloom are different, or that the effects of more widespread drivers may be manifested differently at each locale, our priority at population control/use needs to be more basic research on jellyfish. While we do not advocate a no-fishing approach, we emphasize the need to be cautious in embracing jellyfish fisheries as a panacea and we need to consider the management of each bloom on a case-by-case basis.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 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".