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Record W2113178975 · doi:10.1098/rspb.2010.0399

Calcification provides mechanical reinforcement to whale baleen α-keratin

2010· article· en· W2113178975 on OpenAlexaff
Lawrence Szewciw, D.G. de Kerckhove, G.W. Grime, Douglas S. Fudge

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversity of Guelph
FundersEngineering and Physical Sciences Research Council
KeywordsBaleenBiologyCalcificationWhaleAnatomyEcology

Abstract

fetched live from OpenAlex

Hard alpha-keratins such as hair, nail, wool and horn are stiff epidermal appendages used by mammals in a variety of functions including thermoregulation, feeding and intraspecific competition. Hard alpha-keratins are fibre-reinforced structures consisting of cytoskeletal elements known as 'intermediate filaments' embedded in an amorphous protein matrix. Recent research has shown that intermediate filaments are soft and extensible in living keratinocytes but become far stiffer and less extensible in keratinized cells, and this stiffening may be mediated by air-drying. Baleen, the keratinous plates used by baleen whales during filter feeding, is an unusual mammalian keratin in that it never air dries, and in some species, it represents the most heavily calcified of all the hard alpha-keratins. We therefore tested the hypothesis that whale baleen is stiffened by calcification. Here, we provide, to our knowledge, the first comprehensive description of baleen material properties and show that calcification contributes to overcoming the shortcomings of stiffening this hard alpha-keratin without the benefit of air-drying. We also demonstrate striking interspecies differences in the calcification patterns among three species of baleen whales and provide novel insights into the function and evolution of this unusual biomaterial.

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 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 score0.016
Threshold uncertainty score0.244

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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

Citations84
Published2010
Admission routes1
Has abstractyes

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