MétaCan
Menu
Back to cohort
Record W2033776605 · doi:10.1177/1075547011401631

Graphical and Computationally Intensive Techniques for Presenting and Disseminating Information About the Genetics of Disease

2011· article· en· W2033776605 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Communication · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsDiseaseDisseminationRepresentation (politics)GenomicsGraphical displayInformation DisseminationData scienceMedical geneticsComputer scienceGeneticsBiologyGenomeMedicineWorld Wide WebPolitical scienceGene

Abstract

fetched live from OpenAlex

Exactly how genetic factors contribute to the onset of disease is not fully understood. All the same, information and images pertaining to genetics and disease remain arguably serviceable when they produce agreeable diagnostic, prognostic, and, ultimately, therapeutic results. This article begins with a historical survey of graphical techniques concerning hereditary disease. The article then goes on to show how information gathering and representation broadened steadily to accommodate genetic diagnostic tests. This leads, in a final step, to an examination of the capacity of computational genetics and genomics to generate working models of what causes disease.

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.

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)0.001
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 score0.822
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.316
Teacher spread0.288 · 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