Bibliographic record
Abstract
Abstract There is a critical need for quantitative models that can help evaluate trade‐off decisions related to the impacts of harvesting and protection of aquatic ecosystems within an ecosystem context. Ecosystem models used to evaluate such trade‐offs need to have the capability of capturing the dynamic stability that can arise when predator‐prey interactions are restricted to spatial and temporal arenas. Foraging arenas appear common in aquatic systems and are created by a wide range of mechanisms, ranging from restrictions of predator distributions in response to predation risk caused by their own predators, to risk‐sensitive foraging behaviour by their prey. Foraging arenas partition the prey in each predator‐prey interaction in a food web into vulnerable and invulnerable states, with exchange between these states potentially limiting overall trophic flow. Inclusion of vulnerability exchange processes in models for recruitment processes and food web responses to disturbances like harvesting leads to very different predictions about dynamic stability, trophic cascades and maintenance of ecological diversity than do models based on large‐scale mass action (random mixing) interactions between prey and predators. Although a number of methods to estimate these critical exchange rates are presented, none are considered fully satisfactory. The most important challenge for the practical application of models that incorporate foraging arena theory today is not only developing new or improved methods for measuring exchange rates but also evaluating how such rates vary in responses to major fishery‐induced changes in abundances of predators.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".