Modeling Production of Longline Yarding Operations in Coastal British Columbia
Bibliographic record
Abstract
Two Madil 046 skyline yarders, rigged as slackline systems and equipped with Ballenger motorized carriages, were studied by field crews from the University of British Columbia - Forest Operations Group for approximately six months at sites on the west coast of Vancouver Island in British Columbia. A continuous turn element time study, using handheld data recorders was employed to collect data throughout the study. Mean cycle times for the two operations ranged from 11.5 to 13.5 minutes per cycle. Delays contributed at least 20 percent of the cycle time for both operations and were primarily caused by carriage related problems. Average piece size differed by more than 56 percent and created significant differences in overall system productivity. While average cycle time for the two systems differed by at least two minutes, the system with the longer cycle time had higher mean production due to the larger volume per piece and per cycle. The study results strongly suggest that maximizing volume per cycle is critical to maintaining productivity and minimizing costs, even though cycle time may be increased. In this case, one system was able to capitalize on larger average piece size to significantly improve hourly production, even though cycle times for this yarder were higher. More effort is needed during operations to monitor volume (or weight) per cycle and consistently maintain the maximum volume per cycle for existing conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".