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Record W3114333325 · doi:10.1093/geroni/igaa057.2944

GSA COVID-19 Task Force: Supporting Those in Practice

2020· article· en· W3114333325 on OpenAlexaboutno aff
Lisa M. Brown

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Variety (cybernetics)Coronavirus disease 2019 (COVID-19)Task forceProcess (computing)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakProcess managementPsychologyMedicineComputer sciencePolitical scienceBusinessManagementVirologyArtificial intelligencePublic administrationEconomics

Abstract

fetched live from OpenAlex

Abstract The GSA COVID-19 Task Force developed multiple materials to assist older adults, those who care for them, and policy makers in understanding a variety of topics, including aging and immunity, ageism, the process of developing a vaccine, and how to social distance. Members of the group also undertook development of a decision aid on whether to engage in activities outside the home. The process of developing the decision aid according to Ottawa Research Institute guidelines will be explained.

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)0.183
metaresearch head score (Gemma)0.334
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: Editorial · Consensus signal: none
Teacher disagreement score0.183
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1830.334
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0100.005
Scholarly communication0.0090.008
Open science0.0080.022
Research integrity0.0180.016
Insufficient payload (model declined to judge)0.0650.036

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 head0.129
GPT teacher head0.531
Teacher spread0.402 · 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
GenreEditorial

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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