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Record W2022383423 · doi:10.1016/j.dib.2014.09.006

Data set for the proteomic inventory and quantitative analysis of chicken uterine fluid during eggshell biomineralization

2014· article· en· W2022383423 on OpenAlexfundno aff
Pauline Marie, Valérie Labas, Aurélien Brionne, Grégoire Harichaux, Christelle Hennequet‐Antier, Yves Y. Nys, Joël Gautron

Bibliographic record

VenueData in Brief · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
FundersEuropean Regional Development FundConseil Régional du Centre-Val de LoireInstitut National de la Santé et de la Recherche MédicaleInstitut National de la Recherche AgronomiqueAgence Nationale de la RechercheUniversity of Ottawa
KeywordsBiomineralizationProteomicsEggshellMineralization (soil science)ChemistryQuantitative proteomicsBiologyBiochemistryGeneEcology

Abstract

fetched live from OpenAlex

Chicken eggshell is the protective barrier of the egg. It is a biomineral composed of 95% calcium carbonate on calcitic form and 3.5% organic matrix proteins. Mineralization process occurs in uterus into the uterine fluid. This acellular fluid contains ions and organic matrix proteins precursors which are interacting with the mineral phase and control crystal growth, eggshell structure and mechanical properties. We performed a proteomic approach and identified 308 uterine fluid proteins. Gene Ontology terms enrichments were determined to investigate their potential functions. Mass spectrometry analyses were also combined to label free quantitative analysis to determine the relative abundance of 96 proteins at initiation, rapid growth phase and termination of shell calcification. Sixty four showed differential abundance according to the mineralization stage. Their potential functions have been annotated. The complete proteomic, bioinformatic and functional analyses are reported in Marie et al., J. Proteomics (2015) [1].

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.189

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 science0.0010.000
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 head0.143
GPT teacher head0.342
Teacher spread0.199 · 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 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

Citations21
Published2014
Admission routes1
Has abstractyes

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