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Record W4249358778 · doi:10.1002/0471667196.ess5023

Principal Differential Analysis

2004· other· en· W4249358778 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

VenueEncyclopedia of Statistical Sciences · 2004
Typeother
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsMcGill University
Fundersnot available
KeywordsBasis (linear algebra)MathematicsDifferential equationFourier seriesPrincipal (computer security)Set (abstract data type)Applied mathematicsDifferential (mechanical device)Linear differential equationBasis functionPrincipal component analysisMathematical analysisComputer scienceStatistics

Abstract

fetched live from OpenAlex

Abstract Differential equation systems are often a natural way of describing sets of variables changing over time or space. For example, in controlling industrial systems, engineers consider the time course of a set of inputs and outputs for a system such as a chemical reactor with a view to developing useful feedback processes. Principal differential analysis uses functional data to estimate a system of linear differential equations that fit the data. The estimate minimizes an integrated squared error criterion. The coefficient functions that define the linear differential equation are estimated by expansions in terms of basis functions such as Fourier series or B ‐splines, and the estimates are calculated rapidly and without iteration.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.997

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)0.0040.000

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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