Performance improvement of operating rooms at WHSC using simulation and optimization
Bibliographic record
Abstract
rculty of Gr'¿rdu¿rtc Studies of The University of Nlanitob¿r in ¡rarti:rl fìrlfillnlent of the rcquiremcnt of the tlegree of Master of Science Qing Niu02009Pernrissiott hls beelt grantcd to the University of Manitoba Librarics to lencl â copy of this tltcsis/¡rt'ircticulrr, to Libr':rry ¿rnd Archives Can¿dir (LAC) to le nd a copy of this thesisþracticum, ¿rnd to LAC's ngcnt (UMl/ProQuest) to rnicl'ofìlln, scll co¡lies lnd to publish an abstr¿rct of this thesis/¡r racticu rn.This reprocluction or copy of this thesis has been m¿rde ¿rvail¿rblc b-v ¿ruthority of the copyright olvner solely for the put'pose of ¡rriv:rte study ¿rnd rese:ìr'ch, ¿ìnrl mav only be re¡rroduced n¡d copied as ¡lermittetl by cop¡'right l:ru's or rvith express u,ritten authoriz¿rtio¡r from the co¡lvright olyncl.. B),
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".