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Record W2900302900 · doi:10.1038/sdata.2018.244

X-ray computed microtomography of Megachirella wachtleri

2018· article· en· W2900302900 on OpenAlexafffund
Tiago R. Simões, Michael W. Caldwell, Mateusz Tałanda, Massimo Bernardi, Alessandro Palci, Oksana Vernygora, Federico Bernardini, Lucia Mancini, Randall L. Nydam

Bibliographic record

VenueScientific Data · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of Alberta
FundersKillam TrustsNatural Sciences and Engineering Research Council of CanadaMidwestern University
KeywordsX-ray microtomographyComputed tomographyGeologyMedicineRadiology

Abstract

fetched live from OpenAlex

Understanding the origin and early evolution of squamates has been a considerable challenge given the extremely scarce fossil record of early squamates and their poor degree of preservation. In order to overcome those limitations, we conducted high-resolution X-ray computed tomography (CT) studies on the fossil reptile Megachirella wachtleri (Middle Triassic, northern Italy), which revealed an important set of features indicating this is the oldest known fossil squamate in the world, predating the previous oldest record by ca. 75 million years. We also compiled a new phylogenetic data set comprising a large sample of diapsid reptiles (including morphological and molecular data) to investigate the phylogenetic relationships of early squamates and other reptile groups along with the divergence time of those lineages. The re-description of Megachirella and a new phylogenetic hypothesis of diapsid relationships are presented in a separate study. Here we present the data descriptors for the tomographic scans of Megachirella, which holds fundamental information to our understanding on the early evolution of one of the largest vertebrate groups on Earth today.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.250
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2018
Admission routes2
Has abstractyes

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