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Record W19546019 · doi:10.1016/j.jocd.2009.05.001

Language Identification Strategies for Cross Language Information Retrieval.

2010· article· en· W19546019 on OpenAlexaboutno aff
Alessio Bosca, Luca Dini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceNatural language processingArtificial intelligenceLanguage identificationIdentification (biology)Task (project management)Natural languageInformation retrievalGrammarLanguage modelMetadataLinguisticsWorld Wide Web

Abstract

fetched live from OpenAlex

Androgen ablation (AA) therapy is one of the modalities used to treat prostate cancer. It is well known that AA therapy increases the risk of osteoporosis and fractures. In 2004, the British Columbia Cancer Agency published guidelines regarding bone health in these patients. A key recommendation was to arrange for bone mineral density (BMD) testing if AA was to be used for 6 mo or longer. Our objective was to evaluate how well these guidelines were implemented by reviewing the number of BMDs performed in patients who had been treated at one of the 4 cancer centers in British Columbia. We found that the overall number of BMDs documented after the implementation of the guidelines was significantly greater than the number documented before (25% vs 7.5%, p value < 0.0001). There appeared to be regional differences in implementation, with the greatest effect seen at the Vancouver center, which serves as the chief academic center for the province. The greater effect of guidelines at this center suggests a need for more effective dissemination peripherally. The care gap remaining at even the most impacted center indicates a need for greater efforts to both implement guidelines and monitor their implementation over time.

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.

How this classification was reachedexpand

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score1.000

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 communication0.0010.004
Open science0.0010.000
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 head0.008
GPT teacher head0.310
Teacher spread0.302 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations12
Published2010
Admission routes1
Has abstractyes

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