Ontogenetic scaling of scansorial surface area and setal dimensions of <i>Chondrodactylus bibronii</i> (Gekkota: Gekkonidae): testing predictions derived from cross‐species comparisons of gekkotans
Bibliographic record
Abstract
Abstract Little is known of how the adhesive apparatus of gekkotans scales with growth. Cross‐species comparisons of certain characteristics, using size as a comparator to investigate scaling relationships, suggest certain relationships between subdigital pad area and body size. The manner in which the adhesive apparatus grows and scales within any one species, however, remains unknown, and it is unclear whether interspecific and intraspecific patterns are similar. To address this, we examined a post‐hatching ontogenetic series of the southern African gecko Chondrodactylus bibronii and demonstrate that setal density, setal basal diameter and setal spacing remain relatively constant in relation to size, indicating conserved subdigital pad assembly rules that are independent of size. Conversely, however, average and maximal setal lengths increase slightly and isometrically with size, an outcome that is probably explained by setal row recruitment, and the surface area of the subdigital pads scales close to, but below, isometry with respect to body mass and snout–vent length, it therefore does not increase sufficiently with size to compensate for the increase in mass. As a result, relative adhesive capacity decreases with growth with a regression slope of –0.45.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".