Stand-specific working methods for harvester operators: a simulation study
Bibliographic record
Abstract
Working methods used by harvester operators greatly affect their productivity. However, there might be further improvements in the ability to use multiple working methods by choosing the right method for the stand being harvested. The aim of this study was to assess the productivity of multiple working methods in an array of stand characteristics and to quantify the productivity gains of adapting the operator’s working method to the stand. To do so, we developed a discrete-event simulation model of a harvester. In total, 36 working methods were simulated in 50 different forest corridors of varying tree densities and tree heights, in clear-cut scenarios. While we observed differences in productivity of up to 18.56% between working methods in the same corridor, no method outperformed the others in all conditions. Some working methods were found to be unproductive in every stand condition, and the largest productivity gains came from avoiding them. The upper bound of the productivity gains from adapting the working method to the stand was 2.66%. These results suggest adapting the working method to the stand may not be worthwhile for harvester operators.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".