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Record W2043667680 · doi:10.1007/s10729-011-9169-4

ORchestra: an online reference database of OR/MS literature in health care

2011· article· en· W2043667680 on OpenAlexfundno aff
Peter J. H. Hulshof, Richard J. Boucherie, J. Theresia van Essen, Erwin W. Hans, Johann L. Hurink, Nikky Kortbeek, Nelly Litvak, Peter T. Vanberkel, Egbert van der Veen, Bart Veltman, Ingrid Vliegen, Maartje E. Zonderland

Bibliographic record

VenueHealth Care Management Science · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsnot available
FundersUniversity of TorontoStichting voor de Technische WetenschappenNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsHealth informaticsChoirHealth administrationDatabaseComputer scienceHealth care managementWorld Wide WebInformation retrievalMedicinePublic healthPsychologyNursing

Abstract

fetched live from OpenAlex

We introduce the categorized reference database ORchestra, which is available online at http://www.utwente.nl/choir/orchestra/.

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 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)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0360.039
Science and technology studies0.0010.001
Scholarly communication0.0070.005
Open science0.0030.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0630.038

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 head0.202
GPT teacher head0.473
Teacher spread0.271 · 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.

Study designNot applicable
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

Citations34
Published2011
Admission routes1
Has abstractyes

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