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Record W3071135787 · doi:10.1016/j.jat.2020.105472

Recovery guarantees for polynomial coefficients from weakly dependent data with outliers

2020· article· en· W3071135787 on OpenAlexafffund
Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel Ward

Bibliographic record

VenueJournal of Approximation Theory · 2020
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsUniversity of WaterlooDalhousie University
FundersAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsMathematicsConcentration inequalityMixing (physics)OutlierErgodic theoryApplied mathematicsMarkov chainSampling (signal processing)Matrix (chemical analysis)PolynomialFunction spaceAlgorithmMathematical optimizationComputer scienceDiscrete mathematicsMathematical analysisStatisticsFilter (signal processing)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0020.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.236
Teacher spread0.207 · 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 designTheoretical or conceptual
Domainnot available
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

Citations9
Published2020
Admission routes2
Has abstractno

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