Bibliographic record
Abstract
Only now, as innumerable species of fish face extinction, are we realizing that their supply is not inexhaustible. The industrial revolution spawned trawlers that were no longer reliant on the natural elements for their power and hoovered up the vast, easily accessible supplies of coastal biomass. Today, the well-oiled machine of commercial fishing, pressed on by the economic imperatives of national fishery departments, forges ever further into deeper water and distant latitudes, laying waste to entire marine ecosystems. When fish stocks began collapsing all over the globe from the mid-twentieth century onwards, greater attention was paid to the effects of vast mechanized ships, totally removing entire webs of biodiversity and indiscriminately damaging habitats with fishing equipment. As ever, implementation has fallen far short of global agreements on quotas and protected marine areas. Governments either do not realize the implications of what marine scientists are telling them or are beholden to powerful fisheries lobbies. At the present rate, industrial fishing will continue to decimate fish populations, with insufficient time for overexploited populations to recover. This will have tragic effects on the diversity of cascading marine food chains, with predictable, and inevitable, consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.237 | 0.165 |
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".