Suitable forestry machines for mechanical fuel load reduction and salvage recovery: a short review
Bibliographic record
Abstract
Forest fires are one of the most severe incidents that can destroy large areas of forest lands and cause substantial damages to the nearby residential areas and farmlands. Human efforts to reduce the likelihood of starting forest fires include prescribed burning, mechanical fuel load reduction and livestock grazing. Following natural disasters in forest areas, a salvage recovery operation is usually conducted that may cause some environmental impacts. This article aimed to review the literatures to provide a summary of different types of machines and working methods that have been studied in different countries. The review results showed that the most common method for mechanical fuel load reduction was mechanised cut-to-length using a harvester and a forwarder. This method has been widely applied in Canada, USA, Europe, and Oceania. In some countries (e.g. Australia and USA) the whole tree method is also applied for mechanical fuel load reduction. This method was often conducted using feller-bunchers, grapple skidders and chippers/grinders. The majority of mechanical fuel load reduction projects produced the extra fibre which could be used for bioenergy purposes. The number of salvage recovery studies was limited compared with studies on mechanical fuel load reduction. Salvage recovery operations applied a cut-to-length and whole tree harvesting methods. Windthrown trees were extracted to the roadside using heavy size forwarders or grapple skidders in flat/moderate terrains while tower yarders were applied in steep terrains. Detailed information on work productivity is described in this article which can be of use to the academic and industrial users. 
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".