Ecological geography as a framework for a transition toward responsible fishing.
Bibliographic record
Abstract
Meeting the widely expressed requirement that fisheries should somehow be managed on an 'ecosystem basis' implies that fisheries-relevant ecological processes, and the fisheries themselves, need to be documented in the form of maps. This allows recovery, in intuitive fashion, of at least some of the many dimensions of the complex ecosystems in which the fisheries are embedded. The implied transition, in fisheries science, from bivariate time series, to maps as major heuristic devices has a number of implications - some obvious, some less so - of which a number are here discussed and illustrated. Among the issues covered are: (i) the requirement for a consensus taxonomy of large marine ecosystems; (ii) the need to construct fisheries catch maps in the absence of positive records of what was caught where; (iii) the proper identification of one's audience; and (iv) the mapping of marine protected areas and reserves. The seriousness of the fisheries crisis is emphasized, and the case is made that fisheries, if ever they are going to achieve some measure of sustainability - however defined - ultimately will have to be limited not only through the amount of effort they can effectively deploy, but also limited in space, leading to a change to the defaults under which fisheries operate, currently set such that all aquatic wildlife can be exploited, if under some restrictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".