<i>Diictodon feliceps</i>(Therapsida, Dicynodontia): bone histology, growth, and biomechanics
Bibliographic record
Abstract
Four distinct stages have been identified in the ontogeny of Diictodon feliceps based on variations in cortical thickness, organization of primary osteons, porosity, incidence of growth marks, and extent of endosteal reconstruction. In general, the cortex comprises fibrolamellar bone tissue suggesting rapid bone deposition and growth. In the initial two stages of growth, when up to 70% of adult size is attained, no growth marks interrupt the fibrolamellar tissue, indicating a sustained, fast growth. Growth marks appear late in ontogeny and indicate periods of slow or complete cessation of growth after which, fast growth resumed. Although growth was interrupted and much slower in the later stages, D. feliceps probably had an indeterminate growth strategy. Variations in cortical thickness, porosity, organization of primary osteons, and incidence of annuli and LAGs are observed between elements of the same individual, suggesting variable inter-elemental growth. The propodials had faster appositional growth than did the epipodials, with the humerus having grown much faster than the femur in the same individual. The high cortical thickness (RBT) and correspondingly low k values suggest that the limbs were selected for impact loading. The relatively high RBT and low k value of the humerus compared to other limb bones indicates that the forelimbs were more modified for postural support and digging.
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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 | 0.000 |
| 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 | 0.000 |
| 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".