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Record W4393701562 · doi:10.5281/zenodo.5517235

The Nova Scotia Disease Knowledge Graph

2023· dataset· en· W4393701562 on OpenAlexaffabout
Enayat Rajabi

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsCape Breton University
Fundersnot available
KeywordsNova scotiaNova (rocket)GraphGeographyComputer scienceEngineeringArchaeologyTheoretical computer scienceAeronautics

Abstract

fetched live from OpenAlex

Nova Scotia's government has an abundance of resources in terms of data and information. All this data has been collected and stored on the NSOD portal (https://data.novascotia.ca) in the form of datasets. The Nova Scotia Open Data Portal was built and managed by Socrata API (https://dev.socrata.com). We transformed the disease-related datasets of Nova Scotia Open Data into RDF, enriched them by disease ontology, and it is available to be used under the MIT licence. - Added DBpedia linking

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.025

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 head0.039
GPT teacher head0.290
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes2
Has abstractyes

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