Exploring ultimate hypotheses to predict diel vertical migrations in coregonid fish
Bibliographic record
Abstract
Evolutionary hypotheses for diel vertical migrations (DVM) of aquatic animals include foraging opportunity, predator avoidance, and bioenergetics efficiency. Here we test which hypothesis predicts DVM in the small planktivorous coregonids vendace, Coregonus albula, and Fontane cisco, Coregonus fontanae, in a deep oligotrophic lake. Densities and population depths of young-of-the-year and larger coregonids were determined by hydroacoustics during day and night over 10 consecutive months. Depth distributions of predator-like fishes and zooplankton resources were recorded as well. Furthermore, Secchi depth, water temperature, oxygen concentrations, and pH values were determined at each sampling month. A DVM of the coregonids was observed in all months. Population depths during the night were significantly correlated to water temperatures, oxygen concentrations, and pH values. In contrast, the vertical distributions of predators or resources were not correlated with the coregonid depth distribution. These results do not correspond to the feeding opportunity or predator avoidance hypotheses of DVM, but support in part the bioenergetics efficiency hypothesis. However, the stable migration pattern of fishes over all months despite substantial changes in biotic and abiotic conditions suggests that diel migrations in the coregonids are a genetically fixed behavioral trait to minimize the anticipated potential predation risk in the illuminated water layers during daytime.
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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 | 0.001 | 0.002 |
| 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".