MétaCan
Menu
Back to cohort
Record W2031508940 · doi:10.1371/journal.pone.0039323

Analyzing Taphonomic Deformation of Ankylosaur Skulls Using Retrodeformation and Finite Element Analysis

2012· article· en· W2031508940 on OpenAlexafffund
Victoria M. Arbour, Philip J. Currie

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of Alberta
FundersUniversity of Massachusetts AmherstDinosaur Research InstituteKillam TrustsUniversity of California, San DiegoUniversity of AlbertaAmerican Museum of Natural History
KeywordsTaphonomyDeformation (meteorology)Finite element methodGeologyIntraspecific competitionSkullPaleontologyBiologyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Taphonomic deformation can make the interpretation of vertebrate fossil morphology difficult. The effects of taphonomic deformation are investigated in two ankylosaurid dinosaur taxa, Euoplocephalus tutus (to investigate effects on our understanding of intraspecific variation) and Minotaurasaurus ramachandrani (to investigate the validity of this genus). The ratio of orbit maximum rostrocaudal length to perpendicular height is used as a strain ellipse, which can be used to determine if ankylosaur skull fossils have been dorsoventrally compacted during fossilization and diagenesis. The software program Geomagic is used to retrodeform three-dimensional (3D) digital models of the ankylosaur skulls. The effects of sediment compaction are modeled using finite element analysis, and the resulting strain distributions are compared with the retrodeformed models as a test of the retrodeformation method. Taphonomic deformation can account for a large amount of intraspecific variation in Euoplocephalus, but finite element analysis and retrodeformation of Minotaurasaurus shows that many of its diagnostic features are unlikely to result from deformation.

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 score0.197
Threshold uncertainty score0.396

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 studies0.0000.000
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 head0.044
GPT teacher head0.213
Teacher spread0.169 · 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

Citations62
Published2012
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONESame topicPaleontology and Evolutionary BiologyFrench-language works237,207