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The Phenogenomics of Craniofacial Shape

2012· article· en· W3177275407 on OpenAlexaff
Benedikt Hallgrímsson, Heather A. Jamniczky, Neus Martínez‐Abadías, Nathan M. Young, Ralph Marcucio

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCraniofacialEvolvabilityBiologyEvolutionary biologyVariation (astronomy)VertebrateEvolutionary developmental biologySkullPhenotypeDevelopmental biologyAnatomyGeneticsGene

Abstract

fetched live from OpenAlex

Developmental biology has only recently begun to focus on the mechanisms that generate variation within species. Addressing this difficult question can inform our understanding of dysmorphology. It is also central to the developmental basis for evolvability. Here we review our work on the developmental determinants of shape and size variation in the vertebrate craniofacial complex. We present analyses of the Collaborative Cross founder strains and crosses, mouse mutants and human cranial and facial morphometric data to show that variation in the mammalian skull tends to be structured along axes of covariation. Such axes relate to variation in key developmental processes such as chondrocranial or brain growth, the outgrowth of the facial prominences, and the allometric effects of overall cranial growth. Identifying such key developmental processes is an important first step towards unraveling the complex developmental‐genetic determinants of phenotypic variation. Proceeding beyond this point requires complementary strategies. One is the development of methods for quantitative integration across levels of the genotype‐phenotype map. Another is the use of predictive simulation of complex developmental processes. We present our progress towards these goals and how these strategies can inform our understanding of the developmental basis for phenotypic variation in the vertebrate craniofacial complex.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.554

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.292
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 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

Citations1
Published2012
Admission routes1
Has abstractyes

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