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Record W1966566700 · doi:10.1145/1394042.1394096

Detecting polynomial perfect powers (abstract only)

2008· article· en· W1966566700 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

VenueACM communications in computer algebra · 2008
Typearticle
Languageen
FieldComputer Science
TopicNumerical Methods and Algorithms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLacunary functionPolynomialPerfect powerMathematicsFinite fieldInteger (computer science)Power (physics)Field (mathematics)Discrete mathematicsCombinatoricsComputer sciencePure mathematics

Abstract

fetched live from OpenAlex

We consider the problem of determining whether a polynomial f is a perfect power of another polynomial h, i.e. whether f = hr for some integer r > 0. In particular, we seek algorithms which take advantage of the sparsity in f, and so will be fast when the input is lacunary, with only the nonzero terms represented. We give such algorithms, for polynomials with finite field or rational coefficients, after first showing that in any non-trivial case the power r must be small. An implementation is given in the C++ library NTL, and we compare our results to the fastest-known "dense" algorithm.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.991
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0060.003
Research integrity0.0000.001
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 head0.059
GPT teacher head0.323
Teacher spread0.265 · 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