Morphology-performance Mapping Determines Least Functional Resistance in Morphospace: A Case of Dual Locomotor Systems in Chinese Sturgeon
Bibliographic record
Abstract
Morphology-performance (M-P) mapping seems to be predictive of how morphology may evolve along “functional lines of least resistance”, regardless of genetic effect, but as yet it is not clear the strict (or mathematical) definition of the least functional resistance and how M-P mapping determines it. We recorded station-holding and swimming performance using a published critical swimming speed (Ucrit) test in Chinese sturgeon Acipenser sinensis, and used Ucrit as a proxy for fitness. We addressed a geometric morphometric framework to initiate Arnold’s “morphology-performance-fitness” path model (Arnold, 1983), assessing and visualizing apparent and direct M-P mapping. We quantified them as that one unit of the most-performance-covaried shape vector generated fapparent and f units of standardized performance, respectively. We defined the least functional resistance as the theoretically minimum morphological variation on an arbitrary direction in morphospace required by one unit of standardized fitness, (|b-1|min), which was calculated as a multivariate function of direct M-P mapping (f), together with the angle between paired most-performance-covaried shape vectors in morphospace (θ) and direct performance effect on fitness (w), given dual performance systems. The simulated and empirical data suggested that |b-1|min was constrained by larger sum of absolute direct effects (|fhold| + |fswim|) and absolute functional interaction (|fhold fswim cos θ|), and its direction was biased by magnitude of direct M-P mapping.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".