Morphology and motor behavior of endemic fishes in the upper reaches of the Yangtze River basin
Bibliographic record
Abstract
Dam construction alters the hydrodynamic conditions, consequently impacting the swimming behavior of fish. To explore the effect of flow hydrodynamics on fish swimming behavior, five endemic fish species in the upper Yangtze River basin were selected. Through high-speed video visualization and computer analysis, these species' swimming patterns under different flow velocities (0.1-1.2 m/s) were investigated. The kinematic and morphological characteristics of the fish were presented. The principal component analysis was used to analyse the main factors influencing the swimming ability of fish and to determine the correlation coefficients among fish behavior indicators. Fish exhibited three different swimming patterns under different flow velocities. Low velocity (0.1-0.3 m/s) corresponds to free motion, middle velocity (0.4-0.7 m/s) corresponds to cruising motion, and high velocity corresponds to stress motion (0.8-1.2 m/s). The fish kinematic index curves were obtained, and four of five fish species showed two extreme points, which means the optimal and adverse swimming strategies can be determined. With the increase in flow velocity, the tail-beat frequency showed an increasing trend, whereas the tail-beat angle and amplitude showed a decreasing trend. Morphological and kinematic parameters were the two main indexes that affect the swimming ability of fish, which accounts for 41.9% and 26.9%, respectively.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".