Efficiency measurement of Ontario's sawmills using bootstrap data envelopment analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Sawmills in Ontario are an important forest products industry, contributing to the economic prosperity of the entire province. However, these sawmills have been facing extreme competitive pressures, impacting their operational efficiency. This study uses a nonparametric technique, the bootstrap data envelopment analysis, to analyse the relative efficiencies of 125 Ontario sawmills over a period of 17 years (1999 to 2015). The results indicate low levels of overall technical and managerial efficiencies in Ontario sawmills, which have been further impacted by economic downturns. Further analysis reveals that the size of the sawmills has had a statistically significant impact on their relative technical efficiencies. The main source of inefficiency was the management of operations, particularly when these sawmills were not able to adjust their inputs with changing and uncertain market demand conditions. These results provide policymakers and sawmill managers with comprehensive details so that future resources can be reallocated to improve the performance of the Ontario forest products industry.
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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 | 0.032 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it