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Functional Components of Variation in Handwriting

2000· article· en· W2021856897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Statistical Association · 2000
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsHandwritingScripting languageSmoothingComputer scienceReplication (statistics)Variation (astronomy)Sample (material)Function (biology)Forcing (mathematics)Differential equationProcess (computing)Pattern recognition (psychology)MathematicsArtificial intelligenceStatisticsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Functional data analysis techniques are used to analyze a sample of handwriting in Chinese. The goals are (a) to identify a differential equation that satisfactorily models the data's dynamics, and (b) to use the model to classify handwriting samples taken from differential individuals. After preliminary smoothing and registration steps, a second-order linear differential equation, for which the forcing function is small, is found to provide a good reconstruction of the original script records. The equation is also able to capture a substantial amount of the variation in the scripts across replication. The cross-validated classification process is 100% effective for the samples analyzed.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.123

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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