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Record W4405822422 · doi:10.1002/jmor.70017

Modifiable Clinical Dental Impression Methods to Obtain Whole‐Mouth and Detailed Dental Traits From Vertebrates

2024· article· en· W4405822422 on OpenAlexaff
Johannes Nicolaus Wibisana, Ray A. Sallan, Towa Ota, Pavel Puchenkov, Tai Kubo, Lauren Sallan

Bibliographic record

VenueJournal of Morphology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Toronto
FundersOkinawa Institute of Science and Technology Graduate University
KeywordsDentitionBiologyContext (archaeology)VertebrateImpressionFish <Actinopterygii>AnatomyEvolutionary biologyComputer sciencePaleontologyGenetics

Abstract

fetched live from OpenAlex

Dental impressions, developed for accurate capture of oral characteristics in human clinical settings, are seldom used in research on nonlivestock, nonprimate, and especially nonmammalian vertebrates due to a lack of appropriate tools. Studies of dentitions in most vertebrate species usually require euthanasia and specimen dissection, microCT and other scans with size and resolution tradeoffs, and/or ad-hoc individual impressions or removal of single teeth. These approaches prevent in-vivo studies that factor in growth and other chronological changes and separate teeth from the context of the whole mouth. Here, we describe a non-destructive method for obtaining high-resolution dentition-related traits that can be used on both living animals and museum specimens for almost all vertebrates, involving a customizable and printable dental impression tray. This method has repeatedly and accurately captured whole-mouth morphology and detailed features at high resolution in the living non-teleost actinopterygian fish, Polypterus senegalus, in a laboratory setting. It can be used for comparative morphology and to observe temporal changes such as the presence of microwear, tooth replacement rates, and occlusal and morphological changes through ontogeny.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
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
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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