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Record W4386360739 · doi:10.3847/25c2cfeb.ea84c45e

Next Generation Machine to Study Heliophysics in the Laboratory

2023· article· en· W4386360739 on OpenAlexaff
S. Dorfman, Emily Lichko, Joseph Olson, James Juno, Evdokiya Kostadinova, David Schaffner, Mel Abler, Saikat Chakraborty Thakur, P. V. Heuer, Alfred Mallet, Feiyu Li, G. G. Howes, Jonathan Squire, Douglass Endrizzi, Rachel Young, D. B. Schaeffer, K. G. Klein, Rachael Filwett, Yeimy J. Rivera, Silvina Guidoni, Arian Timm, Jason TenBarge, Lorin Matthews, Lev Arzamasskiy, T. F. Du, Luca Comisso, F. Effenberg, Dan Fries, Peiyun Shi, J. L. Verniero, L. Ofman, Romain Meyrand, Kimberly Moreland, Liang Wang, Subash Adhikari, Vincent Ledvina, Steven R. Cranmer, Chuanfei Dong, Chris R. Gilly, Hossein Ghadjari, Juie Shetye, Christopher Light, Ranadeep Sarkar, Yi‐Hsin Liu, M. Swisdak, B. J. Lynch, Anwesha Maharana, Xiangrong Fu, J. A. Wanliss, Pankaj Kumar, Anshu Kumari, Luis Preisser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of Calgary
FundersUniversity of RochesterAuburn University
KeywordsComputer science

Abstract

fetched live from OpenAlex

Whitepaper #099 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: basic research; infrastructure/workforce/other programmatic. Additional topics: ground-based missions/projects; laboratory space plasma physics; research tools and […]

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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0250.008

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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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 designBench or experimental
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

Citations3
Published2023
Admission routes1
Has abstractyes

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