Bibliographic record
Abstract
The 3D laser scanning technique is nowadays more and more applied in the study of skeleton morphology.Complete 3D model of external geometry can be easily obtained by laser scanning.It contains all the external morphologic information,both size and shape,which is the prerequisite for quantitative study of fossil morphology.The materials in this study include specimens of giant panda,Asian black bear,brown bear,and polar bear.Using 3D laser scanning,the complete 3D models of skulls and mandibles of 28 individuals are built.Nine mandible morphological indexes and 15 skull morphological indexes are selected to build mathematical models.We use J48 algorithm of WEKA to build decision trees for pattern classification.23 landmarks of mandibles and 29 landmarks of skulls are sorted,of which 3D coordinates are used as initial variables to tell the morphological differences of mandibles and craniums of the 28 individuals in the principal component analysis.The result shows that the morphology of giant panda's skull and mandible is signifcantly different from the other three ursid forms; the morphology of brown bear's skull and mandible is in between the black bear and polar bear.The overall cranial morphology of Ailuropoda microta is similar to that of giant panda,but also out of the intraspecific variation,which is another evidence of the durophagous feeding behavior of this Early Pleistocene giant panda.The methods of 3D modeling,decision tree and principal components analysis introduced herein promise future application in morphologic studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".