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Record W6908112345 · doi:10.25504/fairsharing.mtkkwk

FAIRsharing record for: Coronavirus and MS Reporting Database

2018· dataset· en· W6908112345 on OpenAlexaboutno aff

Bibliographic record

VenueFAIRsharing.org · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMultiple sclerosisCoronavirus disease 2019 (COVID-19)CoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MEDLINEPandemic

Abstract

fetched live from OpenAlex

This FAIRsharing record describes: COViMS (COVID-19 Infections in MS & Related Diseases) is a joint effort of the National MS Society, Consortium of MS Centers and Multiple Sclerosis Society of Canada to capture information on outcomes of people with MS and other CNS demyelinating diseases (Neuromyelitis Optica, or MOG antibody disease) who have developed COVID-19.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.168
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1680.096

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 head0.158
GPT teacher head0.380
Teacher spread0.222 · 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 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
Published2018
Admission routes1
Has abstractyes

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