Superficial neuromasts facilitate non-visual feeding by larval striped bass (<i>Morone saxatilis</i>)
Bibliographic record
Abstract
To investigate whether mechanoreception is used in non-visual feeding in larval striped bass (Morone saxatilis), the ontogeny of superficial neuromasts along the lateral line was described using the vital stain FM1-43FX and fluorescent microscopy. The number of neuromasts visible along one flank increased from 11 at first feeding [5 to 7 days post-hatch (dph)] to >150 by the juvenile stage (27 dph). A neomycin dose response (0, 1, 2 and 5 mmol l(-1)) was evaluated for neuromast ablation of bass aged 10, 13, 17 and 20 dph. Using these same age groups, the ability of bass to catch Artemia salina prey in both dark and light tank-based feeding trials was compared between larvae with neuromasts ablated using neomycin (5 mmol l(-1)) and controls. Neomycin significantly reduced the incidence of feeding in the light and dark. Among larvae that fed, those in the dark treated with neomycin caught fewer Artemia (~5 prey h(-1); P<0.05) than controls (16 prey h(-1) at 10 dph; 72 prey h(-1) at 20 dph). In the light, by contrast, neomycin treatment had no significant effect on prey capture by larvae age 13 to 20 dph, but did inhibit feeding of 10 dph larvae. Verification that neomycin was specifically ablating the hair cells of superficial neuromasts and not affecting either neuromast innervation, olfactory pits, or taste cells was achieved by a combination of staining with FM1-43FX and immunocytochemistry for tubulin and the calcium binding proteins, S100 and calretinin.
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 | 0.000 |
| 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 | 0.001 |
| 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".