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Record W4317739163 · doi:10.3390/sym15020318

Ratio Data: Understanding Pitfalls and Knowing When to Standardise

2023· article· en· W4317739163 on OpenAlexaff
Chris Bishop, Ian Shrier, Matthew J. Jordan

Bibliographic record

VenueSymmetry · 2023
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Applications
Canadian institutionsUniversity of CalgaryJewish General Hospital
Fundersnot available
KeywordsVariable (mathematics)Value (mathematics)Component (thermodynamics)Computer scienceMetric (unit)MathematicsStatisticsMathematical analysisOperations managementPhysicsEngineering

Abstract

fetched live from OpenAlex

Ratios represent a single-value metric but consist of two component parts: a numerator variable and a denominator variable [...]

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)0.111
metaresearch head score (Gemma)0.497
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.889
Threshold uncertainty score0.589

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.497
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.009
Science and technology studies0.0020.013
Scholarly communication0.0120.022
Open science0.0050.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0070.005

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 head0.371
GPT teacher head0.463
Teacher spread0.092 · 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 designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations23
Published2023
Admission routes1
Has abstractyes

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