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Record W2062587894 · doi:10.1038/sj.clpt.6100060

Biomedical Communications: Collaborative Research in Scientific Visualization, Online Learning, and Knowledge Translation

2007· review· en· W2062587894 on OpenAlexafffund
Linda Wilson‐Pauwels, Jana Bajcar, Nicholas Woolridge, Jodie Jenkinson

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2007
Typereview
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of TorontoThe Wilson Centre
FundersHealth Canada
KeywordsVisualizationData visualizationComputer scienceVisual mediaKnowledge translationScientific communicationVisual communicationEngineering ethicsMedical educationData scienceMultimediaKnowledge managementMedicineLibrary scienceEngineering

Abstract

fetched live from OpenAlex

Collaborative research in biomedical communications investigates the role of visual media in scientific discovery and in patient and health professional education. The spectrum of work is broad and includes the visualization of scientific knowledge and simulation of hypothetical models of health and disease, as well as the design of audience-centered interactive visual media. The work cited supports the notion that research-based visual media can contribute to the core missions of science: discovery, communication, collaboration, and education. Clinical Pharmacology & Therapeutics (2007) 81, 455–459. doi:10.1038/sj.clpt.6100060; published online 18 January 2007

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.521
GPT teacher head0.613
Teacher spread0.092 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations7
Published2007
Admission routes2
Has abstractyes

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