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Record W4415901322 · doi:10.1016/j.isci.2025.113739

Deciphering causes and behaviors: A recurrent pattern of tail injuries in hadrosaurid dinosaurs

2025· article· en· W4415901322 on OpenAlexafffund
Filippo Bertozzo, Darren H. Tanke, Simone Conti, Fabio Manucci, Gareth Arnott, Pascal Godefroit, Alastair Ruffell, Denver W. Fowler, Elizabeth A. Freedman Fowler, Ivan Bolotsky, Yuri L. Bolotsky, Eileen Murphy

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsRoyal Tyrrell Museum
FundersH2020 Marie Skłodowska-Curie ActionsFundação para a Ciência e a TecnologiaHorizon 2020 Framework ProgrammeU.S. Bureau of Land ManagementDinosaur Research InstituteMinistry of Science and Higher Education of the Russian FederationWestern University of Health SciencesAmerican Museum of Natural History
KeywordsDorsumPathologicalVertebral columnCervical vertebraeBiomechanicsMating

Abstract

fetched live from OpenAlex

Elevated frequencies of repeated injuries are identified in the proximo-middle caudal region of hadrosaurid dinosaur tails. The affected vertebrae show healing injuries in the distal region of the vertebral neural spines, but the causes are yet indeterminate. A finite element analysis was performed on a modeled caudal vertebral series to test if such injuries were caused by loading weight. Our results indicate that the deforming stress resulted from the same dorsal force pressing upon a large area of the tail. We scrutinized all possible biological scenarios that could cause the pathological deformation of the bones. The affected area corresponds to the putative position of the cloacal opening, indicating the possibility that the dorsal force might correspond to the action of a mounting male. As such, these potential mating injuries may represent the first indirect evidence of sexual behavior in non-avian dinosaurs, and a novel approach to recognize female individuals.

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 score0.050
Threshold uncertainty score0.991

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 studies0.0000.001
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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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