Country-wide analysis of the potential use of harwarders for final fellings in Sweden
Bibliographic record
Abstract
There is a need to decrease the costs of cut-to-length operations. The harwarder, a one-machine system with the potential to reduce the costs, has been compared to the two-machine system (TMS) at the stand and regional levels but not at the national level, which is important as basis for decision to implement. The objective was therefore to analyze its potential on a large scale in Swedish final fellings. It was evaluated using two modeling approaches in conjunction with data representing around 30% of Sweden’s yearly final fellings from five forestry organizations. The analyses revealed that total costs could be reduced by around 3% if up to 50% of the total volume was logged using harwarders rather than the TMS. This would require the introduction of up to 250 harwarders into machine fleets that currently use only the TMS. The two modeling approaches gave similar results. It was concluded that the harwarder may need to demonstrate greater potential to justify a full-scale implementation in Swedish forestry, but the machine could be improved through technological development, especially through automation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".