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Record W2959559927 · doi:10.1111/pala.12450

Brachiopod shell thickness links environment and evolution

2019· article· en· W2959559927 on OpenAlexaff
Uwe Balthasar, Jisuo Jin, Linda Hints, Maggie Cusack

Bibliographic record

VenuePalaeontology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsWestern University
FundersPalaeontological Association
KeywordsOrdovicianPaleontologyExtinction eventShell (structure)Extinction (optical mineralogy)GeologyCenozoicTaxonStructural basin

Abstract

fetched live from OpenAlex

Abstract While it is well established that the shapes and sizes of shells are strongly phylogenetically controlled, little is known about the phylogenetic constraints on shell thickness. Yet, shell thickness is likely to be sensitive to environmental fluctuations and has the potential to illuminate environmental perturbations through deep time. Here we systematically quantify the thickness of the anterior brachiopod shell which protects the filtration chamber and is thus considered functionally homologous across higher taxa of brachiopods. Our data come from 66 genera and 10 different orders and shows well‐defined upper and lower boundaries of anterior shell thickness. For Ordovician and Silurian brachiopods we find significant order‐level differences and a trend of increasing shell thickness with water depth. Modern (Cenozoic) brachiopods, by comparison, fall into the lower half of observed shell thicknesses. Among Ordovician–Silurian brachiopods, older stocks commonly have thicker shells, and thick‐shelled taxa contributed more prominently to the Great Ordovician Biodiversification but suffered more severely during the Late Ordovician Mass Extinction. Our data highlight a significant reduction in maximum and minimum shell thickness following the Late Ordovician mass extinction. This points towards stronger selection pressure for energy‐efficient shell secretion during times of crisis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.191
Teacher spread0.183 · 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.

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

Citations7
Published2019
Admission routes1
Has abstractyes

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