A Novel Approach: Investigating Drivers behind Skull Size and Shape Variance within the Genus Didelphis through 3D Morphometric Analysis
Bibliographic record
Abstract
In this study, we employed 3D morphometric technology to investigate the various factors influencing cranial size and shape variation within the opossum genus Didelphis. Data acquisition involved scanning specimens on-site at the Royal Ontario Museum, the University of Michigan Ann Arbor’s collections, and the Smithsonian, alongside specimens from the University of Vermont’s own museum. Our investigation examined the relationships of size and shape variance with factors such as sympatry and allopatry, sex, allometry, age classes, and clinal variation. This analysis was facilitated by the use of software packages Stratovan Checkpoint and R, with Mesquite employed for phylogenetic analyses. Data analysis is currently ongoing, and we anticipate obtaining and interpreting results well in advance of the upcoming conference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".