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Record W2156977583 · doi:10.1671/1914-14

<i>Diictodon feliceps</i>(Therapsida, Dicynodontia): bone histology, growth, and biomechanics

2004· article· en· W2156977583 on OpenAlexfundno aff
Sanghamitra Ray, Anusuya Chinsamy

Bibliographic record

VenueJournal of Vertebrate Paleontology · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsnot available
FundersUniversity of Cape TownUniversity of TorontoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsHumerusOntogenyAnatomyBone growthOsteonCortical boneFemurBiologyLong boneHistologyPaleontology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.204
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations75
Published2004
Admission routes1
Has abstractyes

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