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Record W3102431980 · doi:10.1098/rspb.2020.1922

Evidence of parallel evolution in the dental elements of <i>Sweetognathus</i> conodonts

2020· article· en· W3102431980 on OpenAlexafffund
Wyatt Petryshen, Charles M. Henderson, Kenneth De Baets, Emilia Jarochowska

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2020
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsConodontPaleozoicPermianEvolutionary biologyBiologyPaleontologyTraitTrophic levelMorphometricsParallel evolutionPrincipal component analysisEcologyPhylogeneticsComputer scienceBiostratigraphyGene

Abstract

fetched live from OpenAlex

The repeated emergence of similar morphologies in the dental elements of Permian Sweetognathus conodonts has been a hypothesized example of parallel evolution. To test if morphological parallelisms occur between isolated Sweetognathus lineages, this study uses two-dimensional-based geometric morphometrics combined with a revised and expanded phylogeny of Permian Sweetognathus conodonts to quantify dental element trait distributions and compare the phenotypic trajectories between lineages. A hierarchical clustering method was used to identify recurrent species pairs based on principal component scores describing their morphological variation, with the further incorporation of widely used ecological metrics such as limiting similarity and morphological overlap. Our research implies that a major contributor to conodont diversity in Palaeozoic marine trophic networks is the emergence of recurrent parallel morphologies via disruptive and directional selection. This study illustrates the mechanisms through which conodonts achieved their status as hyper-diverse predators and scavengers, contributing substantially to the complexity of Palaeozoic marine communities.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.052
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.111
GPT teacher head0.312
Teacher spread0.201 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Royal Society B Biological SciencesSame topicMorphological variations and asymmetryFrench-language works237,207