<i>Salvelinus namaycush</i>spawning substratum attracts egg predators and opportunists through chemosensory cues
Bibliographic record
Abstract
Two separate field experiments were conducted in a series of small boreal lakes to test for the attraction of egg predators to lake trout Salvelinus namaycush spawning shoals and subsequently to determine whether chemosensory cues attract egg predators to these sites. In the first experiment, minnow traps set on spawning sites captured significantly more egg predators than those set on structurally similar non-spawning sites. Captures of slimy sculpin Cottus cognatus, common shiner Luxilus cornutus, blacknose shiner Notropis heterolepis and virile crayfish Orconectes virilis were more than double on spawning sites relative to non-spawning sites for the two study lakes. To test whether chemosensory cues could attract egg predators to S. namaycush spawning sites, paired minnow traps were placed on eight to 10 sites in each of the three study lakes; one trap contained visually concealed S. namaycush spawning substratum and the other with visually concealed non-spawning substratum. Traps containing spawning substratum consistently captured more fish and had higher mean daily catches than those that contained non-spawning substratum. The combined results demonstrate a greater prevalence of egg predators on S. namaycush spawning shoals that appears to be the result of chemosensory attraction to spawning substratum.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".