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Record W2035512259 · doi:10.1145/333135.333137

Shortest-substring retrieval and ranking

2000· article· en· W2035512259 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 Transactions on Information Systems · 2000
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsComputer scienceRanking (information retrieval)Information retrievalRelevance (law)Boolean conjunctive querySubstringMatching (statistics)Standard Boolean modelPhraseLearning to rankSimple (philosophy)Theoretical computer scienceData miningBoolean expressionAlgorithmBoolean functionAnd-inverter graphSearch engineData structureArtificial intelligenceWeb search queryMathematicsStatisticsWeb query classification

Abstract

fetched live from OpenAlex

We present a model for arbitrary passage retrieval using Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the order of their expected relevance. Features such as phrase matching, truncation, and stemming integrate naturally into the model. Properties of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is comparable to that of standard ranking techniques. Since global statistics are not used, the method is of particular value in distributed environments. Since ranking is based on arbitrary passages, the structural elements to be ranked may be specified at query time and do not need to be restricted to predefined elements.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.428

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 communication0.0000.004
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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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