Individual leadership and boldness in shoals of golden shiners (Notemigonus crysoleucas)
Bibliographic record
Abstract
Abstract Shoals of golden shiners (Notemigonus crysoleucas) often swim along the perimeter of their large indoor tank at dawn and dusk, and can also be trained to anticipate food arrival by swimming directly towards the food source at midday. In this study all golden shiners in six shoals of 8-12 fish were individually marked with a visible implant elastomer, and shoal movement was video taped in order to determine whether some individuals consistently occupied front positions even when all shoal members were of similar size and experience. There were significant correlations between all three times of day (dawn, midday, dusk) in the mean position (from 1 = first at the front of the shoal to 12 = last at the back) occupied by each fish. In each shoal, 1-3 fish were leaders: all three daily times combined, they had more than twice the occupancy rate of the front two or three positions as expected from chance. In subsequent boldness tests there was a tendency (p = 0.096) for these leaders to pass through dark U-shaped tubes more readily than non-leaders. However, after being dipnetted and transferred to a refuge, leaders did not emerge earlier than non-leaders. Individual tendencies to lead may have been underlain by a motivation to feed (which may differ even in fish of similar size and experience) or by intrinsic mobility. On the other hand, a link between leadership and risk-taking remains to be established for captive golden shiners.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".