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Record W2419408947 · doi:10.1038/srep12338

Developmental and evolutionary novelty in the serrated teeth of theropod dinosaurs

2015· article· en· W2419408947 on OpenAlexafffund
Kirstin S. Brink, Robert R. Reisz, Aaron R. H. LeBlanc, Rong Chang, Yao-Chang Lee, C. C. Chiang, Timothy D. Huang, David C. Evans

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
FundersDinosaur Research InstituteUniversity of Toronto
KeywordsTheropodaBiologyEvolutionary biologyMorphology (biology)Biological evolutionAdaptation (eye)ZoologyPaleontologyCretaceous

Abstract

fetched live from OpenAlex

Tooth morphology and development can provide valuable insights into the feeding behaviour and evolution of extinct organisms. The teeth of Theropoda, the only clade of predominantly predatory dinosaurs, are characterized by ziphodonty, the presence of serrations (denticles) on their cutting edges. Known today only in varanid lizards, ziphodonty is much more pervasive in the fossil record. Here we present the first model for the development of ziphodont teeth in theropods through histological, SEM, and SR-FTIR analyses, revealing that structures previously hypothesized to prevent tooth breakage instead first evolved to shape and maintain the characteristic denticles through the life of the tooth. We show that this novel complex of dental morphology and tissues characterizes Theropoda, with the exception of species with modified feeding behaviours, suggesting that these characters are important for facilitating the hypercarnivorous diet of most theropods. This adaptation may have played an important role in the initial radiation and subsequent success of theropods as terrestrial apex predators.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.228
Teacher spread0.198 · 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

Citations77
Published2015
Admission routes2
Has abstractyes

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