Machine availability and productivity during timber harvester machine operator training
Bibliographic record
Abstract
Machine availability and timber harvest productivity in commercial forestry are influenced in part by operator performance. This work aimed to evaluate the behavior of these two variables, machine availability and productivity, during the training period for harvester operators. The study was conducted at a forestry company situated in Brazil. Productivity and machine availability data were collected for 30 individuals who were trained over an 11-month period. Monthly mean data for both variables were compared using Tukey’s test. The analysis revealed a significant difference in productivity and machine availability during the training period, with productivity increasing until 6 months of harvester operator training while machine availability simultaneously decreased. Productivity began at a mean of 9 m3·PMH0 −1, reaching 24 m3·PMH0 −1 at its peak, and stabilizing around 20 m3·PMH0 −1, where PMH0 is productive machine hours. Machine availability started at 84%, decreased to a mean of 78%, and increased to around 88% until the present. Both variables demonstrated a tendency toward stabilization until 9 months of harvester operation training. Given that the harvester operator training period had a significant influence on machine availability and productivity, this study’s results support careful operational planning, staffing, and resource use during this training period.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".