The lateral line confers evolutionarily derived sleep loss in the Mexican cavefish
Bibliographic record
Abstract
Sleep is an essential behavior exhibited by nearly all animals, and disruption of this process is associated with an array of physiological and behavioral deficits. Sleep is defined by changes in sensory gating that reduce sensory input to the brain, but little is known about the neural basis for interactions between sleep and sensory processing. Blind Mexican cavefish comprise an extant surface dwelling form and 29 cave morphs that have independently evolved increased numbers of mechanoreceptive lateral line neuromasts and convergent evolution of sleep loss. Ablation of the lateral line enhanced sleep in the Pachón cavefish population, suggesting that heightened sensory input underlies evolutionarily derived sleep loss. Targeted lateral line ablation and behavioral analysis localized the wake-promoting neuromasts in Pachón cavefish to superficial neuromasts of the trunk and cranial regions. Strikingly, lateral line ablation did not affect sleep in four other cavefish populations, suggesting that distinct neural mechanisms regulate the evolution of sleep loss in independently derived cavefish populations. Cavefish are subject to seasonal changes in food availability, raising the possibility that sensory modulation of sleep is influenced by metabolic state. We found that starvation promotes sleep in Pachón cavefish, and is not enhanced by lateral line ablation, suggesting that functional interactions occur between sensory and metabolic regulation of sleep. Taken together, these findings support a model where sensory processing contributes to evolutionarily derived changes in sleep that are modulated in accordance with food availability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".