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Record W4388526544 · doi:10.1002/ar.25345

The elusive scleral cartilages: Comparative anatomy and development in teleosts and avians

2023· review· en· W4388526544 on OpenAlexafffund
Tamara A. Franz‐Odendaal

Bibliographic record

VenueThe Anatomical Record · 2023
Typereview
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsMount Saint Vincent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScleraAnatomyVertebrateBiologyCartilageComparative anatomyConnective tissueGene

Abstract

fetched live from OpenAlex

The sclera of all vertebrate eyes is comprised of connective tissue, with some organisms developing cartilage within this tissue. A review of the cartilages that have been described in the vertebrate sclera and their anatomical relationships is discussed together with their potential homology. Incorrect terminology erroneously implies similarity in location, development, morphology, and evolution, which may lead some scientists to assume all cartilages in orbit are the same elements when reading the literature. Therefore, new terminology to distinguish the different types of cartilage associated with the vertebrate eye is proposed. The scleral cartilages that are likely homologous to one another and which are situated in the sclera, should be termed scleral cartilages sensu stricto, while other cartilages in the sclera should be termed ocular cartilages. Some of the cartilages also ossify, and these bones should be distinguished from the scleral ossicles. The plasticity of the scleral tissue layer and its range of morphologies from fibrous to cartilaginous connective tissue across different vertebrate lineages are also described. This review also highlights several gaps in our understanding of the vertebrate scleral cartilages, in particular.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.128
GPT teacher head0.396
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes2
Has abstractyes

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