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Record W2778697349 · doi:10.24966/ggs-2485/100002

Embryogenetics: The Coalescence of Genetics and Embryology

2016· article· en· W2778697349 on OpenAlexaff
Sperber Gh

Bibliographic record

VenueGenetics & Genomic Sciences · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmbryologyBiologyAnatomyEvolutionary biologyCognitive sciencePsychology

Abstract

fetched live from OpenAlex

The launching of a new journal combining two previously disparate disciplines heralds a new era in the annals of human anatomy and developmental biology.The recent advances in embryology and genetics are increasingly integrating the interaction of genetic directives and embryological dispensation in the development of "De humani corporis fabrica" [1].The founders of genetics, Mendel, Bateson, Dobzhansky and Muller could not have imagined the impact that their disciplines have had in advancing the developmental phenomena of embryology.Nor would the pioneers of embryology, their names embedded in Meckel's cartilage, the Eustachian tube, the Gasserian ganglion and the Malpighian corpuscles of the kidney have dreamt of their exploratory sciences being driven by genes that were only revealed by the sequencing of the human genome in 2001 [2].Among the first textbooks to meld the two sciences were Scott Gilbert's "Developmental Biology" [3], Wolpert's "Principles of Development" [4] and Sperber's "Craniofacial Embryogenetics and Development" [5].These books integrate embryological phenomena driven by genetic signaling networks.The detailed identification of discrete components of the constantly changing developing embryo by sophisticated selective

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)0.021
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.003
Science and technology studies0.0030.050
Scholarly communication0.0120.017
Open science0.0020.007
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.294
Teacher spread0.271 · 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 designTheoretical or conceptual
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

Citations0
Published2016
Admission routes1
Has abstractyes

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