Comparison of modeling approaches for evaluation of machine fleets in central Sweden forest operations
Bibliographic record
Abstract
There are many factors to consider when deciding which technologies to use in forest operations and how to plan their use. One important factor is the overall cost when choosing between the established two-machine system (TMS) with a harvester and a forwarder, and a one-machine system with a harwarder in final fellings. Such considerations can be done with different model approaches, all of which have their strengths and weaknesses. The aim of this study was to analyze and compare the TMS and harwarder potential using a Detailed Optimization (DO) approach and an Aggregated Heuristic (AH) approach. The main differences are the aggregation of seasons, including machine system teams, and spatial considerations. The analyses were done for one full year of final fellings for a large forest company’s region in central Sweden, containing information necessary for calculating costs for logging, relocation between stands and traveling between the operator’s home bases and the stands. The approaches were tested for two scenarios; when only TMS were available, and when both TMS and harwarders were available. The main results were that the approaches coincided well in both potential to decrease total costs when harwarders where available, and distribution of TMS and harwarders. There were some differences in the results, which can be explained by differences in thecalculation approach. It was concluded that the DO approach is more suitable when detailed analyses are prioritized, and the AH approach is more suitable when a more approximate analysis will suffice or the available resources for making the analysis are more limited.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".