Fisheries, the inverted food pyramid
Bibliographic record
Abstract
Abstract A global assessment of fishing patterns and fishing pressure from 110 different Ecopath models, representing marine ecosystems throughout the world and covering the period 1970–2007, show that human exploitation across trophic levels (TLs) is highly unbalanced and skewed towards low productive species at high TLs, which are around two TLs higher than the animal protein we get from terrestrial farming. Overall, exploitation levels from low trophic species were <15% of production, and only 18% of the total number of exploited groups and species were harvested >40% of their production. Generally, well-managed fisheries from temperate ecosystems were more selectively harvested at higher exploitation rates than tropical and upwelling (tropical and temperate) fisheries, resulting in potentially larger long-term changes to the ecosystem structure and functioning. The results indicate a very inefficient utilization of the food energy value of marine production. Rebuilding overfished components of the ecosystem and changing focus to balancing exploitation across a wider range of TLs, i.e. balanced harvesting, has the potential to significantly increase overall catches from global marine fisheries.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".