A landmarking protocol for geometric morphometric analysis of squamate endocasts
Bibliographic record
Abstract
Landmark-based geometric morphometrics is widely used to study the morphology of the endocast, or internal mold of the braincase, and the diversity associated with this structure across vertebrates. Landmarks, as the basic unit of such methods, are intended to be points of correspondence, selected depending on the question at hand, whose proper definition is essential to guarantee robustness and reproducibility of results. In this study, 20 landmarks are defined to provide a framework to analyze the morphological variability in squamate endocasts. Ten species representing a cross-section of the diversity of Squamata from both phylogenetic and ecological (i.e., habitat) perspectives were considered, to select landmarks replicable throughout the entire clade, regardless of the degree of neuroanatomical resolution of the endocast. To assess the precision, accuracy, and repeatability of these newly defined landmarks, both intraobserver and interobserver error were investigated. Estimates of measurement error show that most of the landmarks established here are highly replicable, and preliminary results suggest that they capture aspects of endocast shape related to both phylogenetic and ecologic signals. This study provides a basis for further examinations of squamate endocast disparity using landmark-based geometric morphometrics.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".