Pathfinder: a tool for operational planning of forest regeneration on clearcuts
Bibliographic record
Abstract
Abstract Effective forest regeneration is essential for sustainable forestry practices. In Sweden, mechanical site preparation and manual planting is the dominating method, but sourcing labour for the physically demanding work is difficult. An autonomous scarifying and planting system (Autoplant) could meet the requirements of the forest industry and, for this, a tool for regeneration planning and routing is needed. The tool, Pathfinder, plans the regeneration and routes based on the harvested production (hpr) files, soil moisture and parent material maps, no-go areas (for culture or nature conservation), digital elevation models (DEM), and machine data (e.g., working width, critical slope, time taken for different turn angles). The overall planting solution is either a set of capacity constrained routes or a continuous route and could be used for any planting machine as well as for traditional scarifiers as disc trenchers or mounders pulled by forwarders. Pathfinder was tested on eleven regeneration areas throughout Sweden, both with continuous routes and routes based on a carrying capacity of 1500 seedlings. The net operation area, species and seedling density suggestions were deemed relevant by expert judgement in the field. The routes provided by Pathfinder were compared with solutions given by two experienced drivers and a third solution based on the actual soil scarification at the site. Total driving distance did not differ significantly between the suggestions, but Pathfinder included less side-slope driving on steep slopes (≥ 27% or 15°) and medium slopes (15–27%). The chosen threshold value for steep slopes (where side-slope driving should be avoided) affects the routing, and a lower threshold means more turning and longer driving distance. Pathfinder is not only a tool for routing of planting machines, but also helps in planning of traditional regeneration by providing a more correct net area and tree species suggestions based on the growth of the previous stand. It also diminishes the risk of severe soil disturbance by excluding the wettest area in the planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".