Feeding in the dark: lateral-line-mediated prey detection in the peacock cichlid <i>Aulonocara stuartgranti</i>
Bibliographic record
Abstract
The cranial lateral line canal system of teleost fishes is morphologically diverse and is characterized by four patterns. One of these, widened lateral line canals, has evolved convergently in a wide range of teleosts, including the Lake Malawi peacock cichlids (Aulonocara), and has been attributed to its role in prey detection. The ability to study Aulonocara in the laboratory provides an opportunity to test the hypothesis that their reported ability to feed on invertebrate prey living in sandy substrates in their natural habitat is the result of lateral-line-mediated prey detection. The goal of this study was to determine whether Aulonocara stuartgranti could detect hydrodynamic stimuli generated by tethered brine shrimp (visualized using digital particle image velocimetry) under light and dark conditions, with and without treatment with cobalt chloride, which is known to temporarily inactivate the lateral line system. Fish were presented with six pairs of tethered live and dead adult brine shrimp and feeding behavior was recorded with HD digital video. Results demonstrate that A. stuartgranti: (1) uses the same swimming/feeding strategy as they do in the field; (2) detects and consumes invertebrate prey in the dark using its lateral line system; (3) alters prey detection behavior when feeding on the same prey under light and dark conditions, suggesting the involvement of multiple sensory modalities; and (4) after treatment with cobalt chloride, exhibits a reduction in their ability to detect hydrodynamic stimuli produced by prey, especially in the dark, thus demonstrating the role of the lateral line system in prey detection.
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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".