Does predation risk influence habitat use by northern redbelly dace<i>Phoxinus eos</i>at different spatial scales?
Bibliographic record
Abstract
This study investigated the relationship between spatial variations in predation risk and abundance of northern redbelly dace Phoxinus eos at both macroscale (littoral v. pelagic zones) and microscale (structured v. open water habitats in the littoral zone) of Canadian Shield lakes. Minnow traps were placed in both structured and open water habitats in the littoral zone of 13 Canadian Shield lakes, and estimates of the relative predation risk of P. eos in both the pelagic and the littoral zones were obtained from tethering experiments. Results showed that (1) the mean abundance of P. eos in the littoral zone was positively correlated with the relative predation risk in the pelagic zone, (2) P. eos preferentially used structured over open water habitats in the littoral zone and (3) this preference was not related to the relative predation risk in the littoral zone but decreased as the relative predation risk increased in the pelagic zone. At the lake level, these results support the hypothesis that P. eos enter the littoral zone to avoid pelagic piscivores. At the littoral zone level, the results do not necessarily contradict the widely accepted view that P. eos preferentially use structured over open habitats to reduce their predation risk, but suggest that flexibility in antipredator tactics (e.g. shelter use v. shoaling) could explain the spatial distribution of P. eos between structured and open water habitats.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".