Cross‐fertilizing aquatic and terrestrial research to understand predator risk effects
Bibliographic record
Abstract
Research that conceptually transcends boundaries between aquatic and terrestrial ecosystems has a long history of increasing insight into ecology and evolution. To stimulate further cross‐fertilization between studies that focus on different ecosystems, we highlight several insights on risk effects—the costs of antipredator behavior—that have emerged in part because of combined advances in aquatic and terrestrial systems. Namely, risk effects (1) are not restricted to structured landscapes where antipredator behavior is easily measurable, (2) can be substantial even when prey experience very low predation rates, (3) are contingent on a three‐way interaction between the hunting mode of the predator, escape tactic of the prey, and features of the landscape/physical environment, and (4) can interact with direct predation (consumption) and resource availability (through its effects on prey energy state) to control consumer population size. We conclude by highlighting the value of exploring differences between aquatic and terrestrial risk effects and offering a prospectus for future studies of antipredator behavior and its ecological importance in both eco‐domains. WIREs Water 2014, 1:439–448. doi: 10.1002/wat2.1039 This article is categorized under: Water and Life > Conservation, Management, and Awareness
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".