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Record W3212990339 · doi:10.1098/rsbl.2021.0383

Growth variability, dimensional scaling, and the interpretation of osteohistological growth data

2021· article· en· W3212990339 on OpenAlexafffund
Thomas M. Cullen, Caleb M. Brown, Kentaro Chiba, Kirstin S. Brink, Peter J. Makovicky, David C. Evans

Bibliographic record

VenueBiology Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of ManitobaRoyal Ontario MuseumRoyal Tyrrell MuseumUniversity of TorontoCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExtant taxonBiologyTaxonScalingEvolutionary biologyVariation (astronomy)Range (aeronautics)PaleontologyEcologyMathematics

Abstract

fetched live from OpenAlex

Osteohistological data are commonly used to study the life history of extant and extinct tetrapods. While recent advances have permitted detailed reconstructions of growth patterns, physiology and other features using these data, they are most commonly used in assessments of ontogenetic stage and relative growth in extinct animals. These methods have seen widespread adoption in recent years, rapidly becoming a common component of the taxonomic description of new fossil taxa, but are often applied without close consideration of the sources of variation present or the dimensional scaling relationships that exist among different osteohistological measurements. Here, we use a combination of theoretical models and empirical data from a range of extant and extinct tetrapods to review sources of variability in common osteohistological measurements, their dimensional scaling relationships and the resulting interpretations that can be made from those data. In particular, we provide recommendations on the usage and interpretation of growth mark spacing/zonal thickness data, when these are likely to be unreliable, and under what conditions they can provide useful inferences for studies of growth and life history.

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)0.001
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 score0.019
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.017
GPT teacher head0.227
Teacher spread0.211 · 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

Citations52
Published2021
Admission routes2
Has abstractyes

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