MétaCan
Menu
Back to cohort
Record W4386360872 · doi:10.3847/25c2cfeb.583dccc0

Agile Collaboration: Citizen Science as a Transdisciplinary Approach to Heliophysics

2023· article· en· W4386360872 on OpenAlexaff
Vincent Ledvina, Laura Brandt, E. MacDonald, N. A. Frissell, Justin Anderson, Thomas Y. Chen, Ryan J. French, Francesca Di Mare, Andrea Grover, K. Battams, K. Sigsbee, Bea Gallardo‐Lacourt, Donna Lach, Joseph A. Shaw, Michael Hunnekuhl, Burcu Kosar, Wayne Barkhouse, Tim Young, Chandresh Kedhambadi, Chuanfei Dong, Doğacan Öztürk, S. G. Claudepierre, Andy Witteman, Jeremy Kuzub, Gunjan Sinha, Erika Palmerio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsBrandon University
Fundersnot available
KeywordsAgile software developmentEngineering ethicsKnowledge managementPolitical scienceComputer scienceEngineeringSoftware engineering

Abstract

fetched live from OpenAlex

Whitepaper #233 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: space weather applications; infrastructure/workforce/other programmatic. Additional topics: space weather research/applications/operations pipeline; state of the […]

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)0.024
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0050.007
Scholarly communication0.0170.011
Open science0.0020.021
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0240.005

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 head0.145
GPT teacher head0.444
Teacher spread0.299 · 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.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicScientific Computing and Data ManagementFrench-language works237,207