Relative Performance Evaluation of Ontario's Sawmills with Bootstrap DEA
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
This study uses a non-parametric technique, Bootstrap Data Envelopment Analysis (DEA), in analyzing the relative technical efficiency of 125 Ontario's sawmills (with 1402 sample data observations) collected over a period of 17 years (1999 to 2015). The results indicate low levels of overall technical and managerial efficiencies in the Ontario's sawmills over the entire study period. The main source of inefficiency of the sawmills was the management of operations, especially when these sawmills were not able to adjust their inputs with changing and uncertain market demand conditions. These results provide policy makers and sawmill managers with comprehensive details of relative technical efficiencies in Ontario's sawmills, so that future resources can be reallocated to improve the performance of forest products industry in Ontario.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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