MétaCan
Menu
Back to cohort
Record W2612566706 · doi:10.1038/ncomms14952

Perinate and eggs of a giant caenagnathid dinosaur from the Late Cretaceous of central China

2017· article· en· W2612566706 on OpenAlexafffund
Hanyong Pu, Darla K. Zelenitsky, Junchang Lü, Philip J. Currie, Kenneth Carpenter, Li Xu, Eva B. Koppelhus, Songhai Jia, Xiao Le, Huali Chuang, Tianran Li, Martin Kundrát, Caizhi Shen

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersChinese Academy of Geological SciencesChina Geological SurveyNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsCretaceousChinaBiologyAbundance (ecology)PaleontologyZoologyGeographyEcologyArchaeology

Abstract

fetched live from OpenAlex

The abundance of dinosaur eggs in Upper Cretaceous strata of Henan Province, China led to the collection and export of countless such fossils. One of these specimens, recently repatriated to China, is a partial clutch of large dinosaur eggs (Macroelongatoolithus) with a closely associated small theropod skeleton. Here we identify the specimen as an embryo and eggs of a new, large caenagnathid oviraptorosaur, Beibeilong sinensis. This specimen is the first known association between skeletal remains and eggs of caenagnathids. Caenagnathids and oviraptorids share similarities in their eggs and clutches, although the eggs of Beibeilong are significantly larger than those of oviraptorids and indicate an adult body size comparable to a gigantic caenagnathid. An abundance of Macroelongatoolithus eggs reported from Asia and North America contrasts with the dearth of giant caenagnathid skeletal remains. Regardless, the large caenagnathid-Macroelongatoolithus association revealed here suggests these dinosaurs were relatively common during the early Late Cretaceous.

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 score0.049
Threshold uncertainty score0.983

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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

Citations57
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicPaleontology and Evolutionary BiologyFrench-language works237,207