Bibliographic record
Abstract
Human involvement in food webs has been profound, bringing about enormous and disproportionate losses of large apex predators on land and in water. The losses have modified or even eliminated concatenations of indirect interactions propagating from predators to herbivores to plants, inter alia. Food webs are a synthesis of bottom-up energy and nutrient flow from plant producers to consumers and top-down regulation of producers by consumers. The trophic cascade is the simplest top-down interaction and accounts for a great deal of what is known about food webs. In three-link cascades, predators suppress herbivores, releasing plants. In longer cascades, predators can suppress smaller mesopredators, releasing their prey animals. Hunting, fishing, and whaling have brought parallel losses of large apex predators to food webs. Without apex predators, smaller mesopredators have often become superabundant, sometimes with unprecedented suppression of their prey, extinctions, and endangerment. Flourishing mesopredators also can reverse the web regulation and suppress apex predators that have become rare owing to hunting and fishing. This can prevent fisheries recovery and lead to persistent alternative ecosystem states. Although food-web modules of large animals are increasingly well understood, the parts of webs consisting of small inconspicuous organisms, such as mutualists and parasites, and webs in obscure places, such as in the soil, are much of the challenge of future research.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".