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Record W2027092274 · doi:10.1007/s11434-013-5765-7

Tooth loss and alveolar remodeling in Sinosaurus triassicus (Dinosauria: Theropoda) from the lower jurassic strata of the Lufeng Basin, China

2013· article· en· W2027092274 on OpenAlexaff
Lida Xing, Phil R. Bell, Bruce M. Rothschild, Hao Ran, Jianping Zhang, Zhiming Dong, Zhang We, Philip J. Currie

Bibliographic record

VenueChinese Science Bulletin · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTheropodaDental alveolusResorptionTooth lossBone remodelingMaxillaAnatomyBiologyDentistryMedicinePathologyCretaceousPaleontology

Abstract

fetched live from OpenAlex

Pathological or traumatic loss of teeth often results in the resorption and remodeling of the affected alveoli in mammals. However, instances of alveolar remodeling in reptiles are rare. A remodeled alveolus in the maxilla of the Chinese theropod Sinosaurus (Lower Jurassic Lower Lufeng Formation) is the first confirmed example of such dental pathology in a dinosaur. Given the known relationship between feeding behavior and tooth damage in theropods (teeth with spalled enamel, tooth crowns embedded in bone) and the absence of dentary, maxillary, and premaxillary osteomyelitis, traumatic loss of a tooth is most likely the cause of alveolar remodeling. Based on the extent of remodeling, the injury and subsequent tooth loss were non-fatal in this individual.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2013
Admission routes1
Has abstractyes

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