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Record W2037382435 · doi:10.1186/1471-213x-8-92

An ontology for Xenopus anatomy and development

2008· article· en· W2037382435 on OpenAlexafffund
Erik Segerdell, Jeff B. Bowes, Nicolas Pollet, Peter D. Vize

Bibliographic record

VenueBMC Developmental Biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsUniversity of Calgary
FundersNational Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAlberta Heritage Foundation for Medical Research
KeywordsXenopusBiologyOntologyModel organismComputational biologyControlled vocabularyBioinformaticsComputer scienceGeneInformation retrievalGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: The frogs Xenopus laevis and Xenopus (Silurana) tropicalis are model systems that have produced a wealth of genetic, genomic, and developmental information. Xenbase is a model organism database that provides centralized access to this information, including gene function data from high-throughput screens and the scientific literature. A controlled, structured vocabulary for Xenopus anatomy and development is essential for organizing these data. RESULTS: We have constructed a Xenopus anatomical ontology that represents the lineage of tissues and the timing of their development. We have classified many anatomical features in a common framework that has been adopted by several model organism database communities. The ontology is available for download at the Open Biomedical Ontologies Foundry http://obofoundry.org. CONCLUSION: The Xenopus Anatomical Ontology will be used to annotate Xenopus gene expression patterns and mutant and morphant phenotypes. Its robust developmental map will enable powerful database searches and data analyses. We encourage community recommendations for updates and improvements to the ontology.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0020.001
Scholarly communication0.0020.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.040
GPT teacher head0.308
Teacher spread0.268 · 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
GenreMethods

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

Citations58
Published2008
Admission routes2
Has abstractyes

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