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Record W1969319738 · doi:10.1111/pala.12165

Pelagic neonatal fossils support viviparity and precocial life history of <scp>C</scp>retaceous mosasaurs

2015· article· en· W1969319738 on OpenAlexafffund
Daniel J. Field, Aaron R. H. LeBlanc, Adrienne Gau, Adam D. Behlke

Bibliographic record

VenuePalaeontology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSmithsonian Institution
KeywordsPelagic zoneBiologyApex predatorPaleontologyFisheryPredation

Abstract

fetched live from OpenAlex

Abstract Mosasaurs were large marine squamates that inhabited all of the world's oceans during the Late Cretaceous. Their success as apex predators has been attributed to their rapid acquisition of aquatic adaptations, which allowed them to become fully pelagic. However, little is known about the breeding biology of derived, flipper‐bearing mosasaurs, as the record of neonatal mosasaur fossils is extremely sparse. Here, we report on the fragmentary cranial remains of two neonatal mosasaurs from the Niobrara Formation, referred to Clidastes sp. Comparison with other preliminary reports of neonatal mosasaurs reveals that these specimens are among the smallest individuals ever found and certainly represent the smallest known Clidastes specimens. The recovery of these extremely young specimens from a pelagic setting indicates that even neonatal mosasaurs occupied open oceanic habitats and were likely born in this setting. These data shed new light on the ecology of neonatal mosasaurs and illustrate the degree to which size‐related taphonomic and collection biases have influenced our understanding of the early life history of these iconic marine reptiles.

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)0.001
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.221
Teacher spread0.190 · 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

Citations21
Published2015
Admission routes2
Has abstractyes

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