Comparison of Ontario’s roundwood and recycled fibre pulp and paper mills’ performance 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
The pulp and paper industry converts roundwood and recycled fibre, collected from wastepaper into printing and writing papers, and other specialty grades of paper. 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 in analyzing and comparing the operational efficiency of the Ontario's pulp and paper mills, with 224 sample data observations over a period of 17 years. The results indicate low levels of overall technical and managerial efficiencies in the pulp and paper mills using recycled fibre. The results of the study highlight that the pulp and paper industry needs to divert their attention to streamlining the manufacturing processes, reducing costs, improving raw material usage, and making capital investments in the new and improved technology, in order to improve the operational efficiency and competitiveness of the Ontario's pulp and paper mills. The pulp and paper mills using recycled fibre require huge capital investments, especially for installing the latest de-inking technology. 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.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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