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Record W4213259218 · doi:10.21203/rs.3.rs-1300162/v1

Hadrosauroid eggs and embryos from the Upper Cretaceous (Maastrichtian) of Jiangxi Province, China

2022· preprint· en· W4213259218 on OpenAlexafffund
Lida Xing, Kecheng Niu, Tzu-Ruei Yang, Donghao Wang, Tetsuto Miyashita, Jordan C. Mallon

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsCanadian Museum of Nature
FundersHigher Education Discipline Innovation ProjectNatural Sciences and Engineering Research Council of CanadaMinistry of Science and Technology, TaiwanNational Natural Science Foundation of China
KeywordsCretaceousHatchlingChinaPaleontologyLimitingAvian clutch sizeBiologyGeologyGeographyEcologyReproductionArchaeologyHatchingEngineering

Abstract

fetched live from OpenAlex

Abstract Background: Dinosaur eggs containing embryos are rare, limiting our understanding of dinosaur development. Recently, a clutch of subspherical dinosaur eggs was discovered while blasting for a construction project in the Upper Cretaceous red beds (Hekou Formation) of the Ganzhou basin, Jiangxi Province, China. At least two of the eggs contain identifiable hadrosauroid embryos, described here for the first time.Results: The eggs, attributable to Spheroolithidae indet., are thick-walled but small (~660 mL) compared to those of Lambeosaurinae. Based on the distinctive shape of the squamosal, the embryos compare favourably with Nanningosaurus dashiensis, otherwise known from incomplete skeletal material from Upper Cretaceous red beds elsewhere in southern China.Conclusion: The small size of the eggs and embryos, similar to those of Hadrosaurinae, indicates that the larger eggs and hatchlings of typical Lambeosaurinae are evolutionarily derived.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

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 communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.308
Teacher spread0.279 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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