Idle times of forest machine and truck fleets in Finnish logging and timber-hauling enterprises
Bibliographic record
Abstract
Abstract In the forest sector, the idle time of machinery has a significant impact on fuel consumption, greenhouse gas (GHG) emissions, and operating costs. This study investigated the idle times of forest machinery and timber trucks in Finland. Study materials were collected through questionnaires from small- and medium-sized logging ( loggers ) (N = 817) and timber-hauling ( timber-haulers ) (N = 339) enterprises (SMEs). The response rate from the loggers was 17.9% (n = 146) and 25.7% (n = 87) from the timber-haulers. The study revealed that only a quarter of the loggers and a half of the timber-haulers were aware of the idle times of their fleets. Of these, 55% of the loggers and 61% of the timber-haulers estimated their idle times at 6–14% of the total operating hours of their fleets. The results also indicated that idle time should be lower compared to current levels: respondents that were aware of idling suggested that 2–5% was a more desirable proportion, while the respondents that were unaware of idling rated desirable idle time at < 2% of total operating time. Respondents who had detected the idle times of their fleet noted significantly more opportunities to reduce idling than those who were unaware of idling. The work phases with the greatest potential to reduce idling times during logging operations were “Planning on the harvesting site” and “Operator breaks”. Correspondingly, “Waiting time at the destination (i.e., timber reception of the mill)” and “Driver breaks” were the phases with the most potential to reduce idling during timber-hauling. This study concluded that there is significant potential to reduce fuel consumption and GHG emissions by improving the awareness of the forest machine operators and truck drivers with regard to the idle times of machinery and vehicles. As such, more research data and operator/driver training are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".