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Record W2156841116

Quadratic Binary Programming Models in Computational Biology

2008· article· en· W2156841116 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAlgorithmic operations research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenome Rearrangement Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsHeuristicsSolverQuadratic equationLinearizationQuadratic programmingBinary numberComputer scienceMathematical optimizationMathematicsApplied mathematicsArithmetic
DOInot available

Abstract

fetched live from OpenAlex

In this paper we formulate four problems in computational mo lecular biology as 0-1 quadratic programs. These problems are all NP-hard, and the current solution methods u sed in practice consist of heuristics or approximation algorithms tailored to each problem. Using test problems fr scientific databases, we address the question, “Can a general-purpose solver obtain good answers in reasonable t ime?” In addition, we use the latest heuristics as incumbent solutions to address the question, “Can a general-purpose s olver confirm optimality or find an improved solution in reasonable time?” Our computational experiments compar e four different reformulation methods: three forms of linearization and one form of quadratic convexification.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.547

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 studies0.0000.000
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 head0.089
GPT teacher head0.376
Teacher spread0.288 · 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