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Record W2316141787 · doi:10.1139/cjz-2013-0231

β-Keratin composition of the specialized spectacle scale of snakes and geckos

2014· article· en· W2316141787 on OpenAlexaffvenue
Kevin L. H. van Doorn, J. G. Sivak, Mathilakath M. Vijayan

Bibliographic record

VenueCanadian Journal of Zoology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
FundersUniversity of South Carolina
KeywordsSpectacleBiologyKeratinGeckoIntegumentZoologyAnatomyGenetics

Abstract

fetched live from OpenAlex

The eyes of snakes and most geckos are shielded beneath a layer of transparent skin (the “spectacle”), of which the outermost layer consists of an optically transparent scale. The characteristics of the spectacle scale that contribute to its transparency are not well understood but may conceivably be related to its biochemical composition. The composition of the spectacle scales of numerous snakes and two geckos was analyzed with particular focus on β-keratins, the hard proteins that form the outermost layer of squamate scales, to determine whether spectacle scales differ biochemically from other scales and whether they differ between species. Results indicate that the spectacle scale of snakes differs in the types of β-keratins it contains and that diversity in spectacle β-keratins occurs between species and bears a relationship with taxonomy, suggesting that optical transparency is not restricted to a few isoforms. Other findings include a greater β-keratin to α-keratin ratio in the embryonic spectacle of pythons compared with those from after hatch and a complete absence of β-keratin in gecko spectacle scales, an unusual characteristic for squamate integument. Expression of β-keratins in the spectacle has clearly evolved for needs specific to this specialized region of the integument.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.342

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)0.0000.000

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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 designBench or experimental
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

Citations4
Published2014
Admission routes2
Has abstractyes

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