On-board computing in forest machinery as a tool to improve skidding operations in South African softwood sawtimber operations
Why this work is in the frame
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Bibliographic record
Abstract
On-board computing (OBC) systems in South African timber-harvesting operations are currently limited because of the lack of local expertise and experience. In this study three trials were conducted, monitoring three skidder extraction operations at three sites in South African softwood sawtimber operations. Both cable and grapple skidders were equipped with a MultiDAT data collection device. For all trials, parallel manual time studies were conducted. The results from the time-studies were then used as the basis for evaluation of the accuracy of the information gathered by the OBCs. The results demonstrated the usefulness of OBC devices. This paper reports the results of the application of various tools to support these assessments, considering different levels of complexity, different detail of the results and different fields of application (business uses and research purpose). With regards to the estimation of the machine utilisation rate, it was highlighted that the use of the vibration sensor with additional input from the machine operator in order to define the reason for stopping offered the best solution with an error ranging from -0.74% to +5.54%. This tool can be used in long-term monitoring to identify the possible under-utilisation of a machine and consequently to establish a working system that provides greater productivity. For the evaluation of machine utilisation, the simple analysis of the GPS track-log also provided good results with an error ranging from 4.29% to -4.25%, but it required time-consuming post-operation processing of the raw data in order to correctly interpret. The use of GPS data to perform work studies in general worked reasonably well, the main problem being again that, at certain stages, the process was not easily automated and tended to require time-consuming post-operation data manipulation. However, because of the amount of information derived from data processing, it presents a useful research tool. The introduction of simple OBCs in South Africa is possible, but does require organisational changes and adaptations from both machine operators, who need to be trained to understand and manage the specific user interface and data transmission, and from management responsible for data storage, collation and analysis, and subsequently the implementation of results in improving forest operations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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