Predicting the productivity of motor-manual workers in precommercial thinning operations
Bibliographic record
Abstract
Precommercial thinning is an important part of intensive management in northern forests. Precommercial thinning is largely carried out by motor manual means, and workers are usually paid on a production basis. To establish a piece-rate system that fairly compensates workers, it is important to accurately predict their production for various site conditions. Based on the observation of 129 workers, a model that predicts the number of hours required to thin one hectare of forest as a function of the number of stems per hectare was developed. It was not possible to detect a statistically significant effect from site factors such as slash, rocks, stumps, and terrain slope. The model is compared with similar attempts reported in the literature. The proposed model will be especially useful to those concerned with labour productivity, compensation systems and benefit-cost analysis in silviculture. Key words: workers' performance, productivity modeling, time consumption, thinning, brushsaw
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".