Relative abilities to correct rolling disturbances of three morphologically different fish
Bibliographic record
Abstract
Responses of three fish species were measured following the addition of neutrally buoyant weight-float combinations that increased rolling instability. The three species were creek chub, Semotilus atromaculatus, largemouth bass, Micropterus salmoides, and bluegill, Lepomis macrochirus. Ability to correct posture was predicted to increase with fin size and body depth in the order creek chub < largemouth bass < bluegill. In a 90-s period, the least added torque causing fish to roll to 90° in response to disturbances, ΔT90, and the least added torque making fish unable to recover from rolling, ΔTcrit, were measured as limits of ability to correct postural disturbances. Contrary to expectations, creek chub required a 58% increase in body torque to reach ΔT90, significantly larger than the 11% increase for largemouth bass and 19% increase for bluegill. Similarly, ΔTcritwas a 78% increase in body torque for creek chub, 43% for largemouth bass, and 34% for bluegill. Increased rolling torques resulted in behaviors reducing and avoiding rolling, including tilting, which reduces metacentric height, inverted swimming, which stabilizes fish, and contacting surfaces, which generates static forces. The superior ability of creek chub to correct postural disturbances may be explained by a fin arrangement that facilitates interactions with the ground.
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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 | 0.001 |
| 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".