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Record W3110705585 · doi:10.1098/rsbl.2020.0750

Convergent dental adaptations in the serrations of hypercarnivorous synapsids and dinosaurs

2020· article· en· W3110705585 on OpenAlexafffund
Megan R. Whitney, Aaron R. H. LeBlanc, Ashley R. Reynolds, Kirstin S. Brink

Bibliographic record

VenueBiology Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of ManitobaUniversity of TorontoUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Geographic Society
KeywordsBiologyDentitionInterdental consonantConvergent evolutionPaleontologyEvolutionary biologyAnatomyPhylogenetic tree

Abstract

fetched live from OpenAlex

Theropod dinosaurs are well known for having a ziphodont dentition: serrated, blade-shaped teeth that they used for cutting through prey. Serrations along the carinae of theropod teeth are composed of true denticles, a complex arrangement of dentine, enamel, and interdental folds. This structure would have supported individual denticles and dissipated the stresses associated with feeding. These particular serrations were previously thought to be unique to theropod dinosaurs and some other archosaurs. Here, we identify the same denticles and interdental folds forming the cutting edges in the teeth of a Permian gorgonopsian synapsid, extending the temporal and phylogenetic distribution of this dental morphology. This remarkable instance of convergence not only represents the earliest record of this adaptation to hypercarnivory but also demonstrates that the first iteration of this feature appeared in non-mammalian synapsids. Comparisons of tooth serrations in gorgonopsians with those of earlier synapsids and hypercarnivorous mammals reveal some gorgonopsians acquired a complex tissue arrangement that differed from other synapsids.

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)0.000
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 score0.015
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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