Modelling of wander ratios, travel speeds and productivity of cable and grapple skidders in softwood sawtimber operations in South Africa
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Bibliographic record
Abstract
The objective of this study was to develop predictive models for cable and grapple skidder wander ratios, travel speeds (loaded and unloaded), and productivity in softwood roundwood sawtimber harvesting operations. For field data collection, the study utilised on-board computing systems supported by manual time study. Four-hundred and twenty-seven extraction cycles over varying terrain and tree sizes were studied for 13 sites in the Western, Southern and Eastern Cape, and KwaZulu-Natal. Machine make and model as well as gross power rating were studied. Due to insignificance across skidder types and configurations, the objective of creating a predictive model for skidder wander ratio was not met. The overall mean wander ratio for all skidders and terrain studied was 1.12:1. This value was used in the models subsequently developed. No differences were found for unloaded and loaded travel speeds between individual skidder types and speeds were found to be 7.3 km h−1 and 5.5 km h−1, respectively. In terms of productivity, when based on field-measured data, there were differences between skidder types with cable skidders achieving 43.9 m3 per productive machine hour (PMH) and grapples skidders 123.9 m3 PMH−1. The study, however, found that both cable and grapple skidders were only hauling approximately 50% of their capacity and for that reason multiple regression models to predict potential production at full payload capacity were developed for the two skidder configurations. Multiple regression was also used to develop prediction models for travel speeds loaded and unloaded. The study met its objectives for driving speeds and productivity, and the developed models will be used in a subsequent network analysis to provide solutions to optimise the softwood sawtimber supply chain. The study also found that the human element had an impact on the factors studied and that good training, planning, implementation and operational control are imperative to ensure supply (or value) chain goals are met.
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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 | 0.001 |
| 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 it