MétaCan
Menu
Back to cohort
Record W4391512525 · doi:10.1142/s0217595924500040

Semi-Additive Integer-Valued Production Technology for Analyzing Public Hospitals in Mashhad

2024· article· en· W4391512525 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

VenueAsia Pacific Journal of Operational Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsYork University
Fundersnot available
KeywordsProduction (economics)Integer (computer science)Integer programmingBusinessComputer scienceMathematicsMathematical optimizationEconomicsMicroeconomicsOperating system

Abstract

fetched live from OpenAlex

Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [Formula: see text] rather than [Formula: see text]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.066
GPT teacher head0.360
Teacher spread0.295 · 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