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Record W4388534267 · doi:10.1016/j.isci.2023.108425

Attaching organic fibers to mineral: The case of the avian eggshell

2023· article· en· W4388534267 on OpenAlexafffund
Daniel J. Buss, Natalie Reznikov, Marc D. McKee

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsMcGill University
FundersRéseau de Recherche en Santé Buccodentaire et OsseuseNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsEggshellMineralChemistryPolymer scienceNanotechnologyEcologyBiologyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Bird eggs possess a mineralized eggshell with a soft underlying fibrous membrane. These dissimilar material layers successfully evolved a structural attachment to each other as a conserved avian reproduction strategy essential to avian embryonic development, growth, and hatching of the chick. To understand how organic membrane fibers attach to shell mineral (calcite), 3D multiscale imaging including X-ray and electron tomography coupled with deep learning-based feature segmentation was used to show how membrane fibers are organized and anchored into shell mineral. Whole fibers embed into mineral across the microscale, while fine mineral projections (granules/spikes) insert into fiber surfaces at the nanoscale, all of which provides considerable surface area and multiscale anchorage at the organic-inorganic interface between the fibrous membrane and the shell. Such a reciprocal anchorage system occurring at two different length scales between organic fibers and inorganic mineral provides a secure attachment mechanism for avian eggshell integrity across two dissimilar materials.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.263
Teacher spread0.233 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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