Ontogenetic changes in chemical alarm cue recognition and fast‐start performance in guppies (<i>Poecilia reticulata</i>)
Bibliographic record
Abstract
Abstract Risk recognition and fast‐start performance are critical to fish survival when faced with predators. Many fish species have been shown to recognize risks associated with chemical cues released by injured conspecifics. However, little is known about the ontogeny of “risk” recognition via damage‐released chemical alarm cues and fast‐start performance in prey fishes. The objectives of this study were to determine whether risk recognition and fast‐start performance in guppies (Poecilia reticulata) exhibit ontogenetic variation, and whether there is a trade‐off between risk recognition and fast‐start performance. To achieve these objectives, individual guppies from replicate groups were assayed on one of the 1st, 7th, 14th, 21st, or 28th day after their birth. We found that both the risk recognition and fast‐start performance in guppies exhibited ontogenetic variation, as on days 1 and 7, fish did not exhibit risk recognition when exposed to alarm cues from conspecifics, but by day 14, such recognition was evident. Noticeable increases in maximum linear velocity (Vmax), maximum linear acceleration (Amax), and escape distance (S120 ms) were concurrent with progressive ontogenetic stage, and no significant correlations were found between risk recognition and fast‐start performance at any ontogenetic stage. Our findings reveal ontogenetic variation in damage‐released chemical cue recognition and fast‐start performance in guppies.
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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 | 0.000 |
| 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".