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.
Bibliographic record
Abstract
Health care operations management has become a major topic for health care service providers and society.Operations research already has and further will make considerable contributions for the effective and efficient delivery of health care services.This special issue collects seven carefully selected papers dealing with optimization and decision analysis problems in the field of health care operations management.The papers cover a considerable range of health care problems including location planning for hospital and health services (Mestre et al., Zhang et al.), organization of hospital resources (Vanberkel et al., Hulshof et al.), surgery scheduling (Marques et al., Herring and Herrmann) and treatment scheduling (Schimmelpfeng et al.).These problems are addressed within a number of different health care environments such as hospitals, preventive care, outpatient clinics and rehabilitation hospitals.The operations research techniques which are employed are mixed-integer linear programming, stochastic dynamic programming, hierarchical decomposition, queueing theory, simulation and choice models.The special issue thus covers a broad range of problems, environments and techniques.It is noteworthy that all papers are either treating a real life problem or are inspired by the latter, which demonstrates the problem-driven
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it