Performance Evaluation of Pulp and Paper Mills: Bootstrap Data Envelopment Analysis Approach
Bibliographic record
Abstract
The pulp and paper industry converts roundwood and recycled fibre, collected from wastepaper into printing and writing papers. The pulp and paper mills in Ontario have been facing extreme competitive pressures, which have affected their performance leading to several mill closures. The purpose of this study is to evaluate and compare the relative performance of three types of Ontario's pulp and paper mills (using all fibre, only roundwood fibre, and only recycled fibre). This study uses bootstrap data envelopment analysis and the results indicate low levels of overall technical and managerial efficiencies. The results of this study provide policy makers with detailed performance analysis so that future input resources can be reallocated to improve the performance of the pulp and paper mills in Ontario. It is recommended that the pulp and paper mills using recycled fibre require huge capital investments to install de-inking technology to improve performance.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".