Performance analysis of primary wood producers in British Columbia using data envelopment analysis
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
Despite its importance, performance assessment of the Canadian primary wood products sector has received little attention in the academic literature and business practices. In this research a nonparametric technique, called data envelopment analysis (DEA), was used to evaluate the performance of sawmills in British Columbia in 2002. Individual mills were inspected using different DEA models to capture their technical, scale, and aggregate efficiencies. Log consumption and labor utilization were considered as the inputs and lumber and chip production as the outputs of these models. Although British Columbia sawmills enjoyed high scale efficiency, only 7% of them were aggregately efficient. The results showed possible efficiency improvements by increasing the production and decreasing the labor usage. Post-hoc analyses with two nonparametric statistical tests, median quartile and KruskalWallis, revealed that the average efficiency of sawmills in different British Columbia forest regions varied significantly; however, the number of operating days had no effect on technical efficiency of sawmills at a 5% significance level.
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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 | 0.028 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.031 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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