An application of data envelopment analysis to investigate the efficiency of lumber industry in northwestern Ontario, Canada
Bibliographic record
Abstract
这研究瞄准探索技术效率伐木在西北的安大略的工业，加拿大使用数据包封分析(DEA ) 。DEA 模型分析相对技术效率伐木有由划分 10 年的时间系列数据的不相称的输入和产量的工厂因为 24 的输入和产量伐木工厂，超过二个时期(19992003 和 20042008 ) 。也就是，材料(日志卷) ，劳动(工时) ，精力(公猪燃料和电) 的二种类型，和材料输出的四输入(伐木卷) 在这研究被使用。趋势分析显示出 10% ， 13% 和 13% 的年度减小为伐木输出，记载消费(输入) 并且分别地，在时期期间雇员数 19992008。从有有精力输入并且没有精力输入的二种情形的 DEA 的结果，因为二个时期被发现被混合并且有趣。当一些工厂在第二个时期以可得到的少见的输入的最好的使用改进了他们的性能时，一些在效率显示出否定 % 变化。在 with 精力输入和 without 精力输入情形，一些工厂从第一个时期在第二个时期在效率显示出减小，与 13.9% 和 47.6% 的最高估计的减小分别地。对在后者时期的工厂的这些否定表演的可能的解释是在在与第一个时期相比的第二个时期的生产的衰落，在这些工厂不能调整他们的输入的地方(主要劳动) 同样比例的解雇期可能一直不是可能的。这些结果向政策制造者和工业股东提供效率的趋势和未来输入的雇用以及重新分配机会的改进理解以便从这个扇区增加好处。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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 source (direct Gemma or distilled Codex), 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".