Logging operations in pine stands in Belgium with additional harvest of woody biomass: yield, economics, and energy balance
Bibliographic record
Abstract
Due to the enhanced demands for woody biomass, it is increasingly relevant to assess possibilities to harvest forest residues in addition to logs. Here, eight strategies for whole-tree harvesting from clearcuts and early thinnings of pine (Pinus nigra Arnold) stands in northern Belgium are evaluated. A detailed cost analysis using the machine-rate method was conducted along with scenario and sensitivity analyses of the variables affecting the harvesting cost. On average, we found much higher revenue for logs than for wood chips from forest residues. In clearcuts, a mobile chipper was more profitable than a roadside chipper. On the other hand, the harvesting cost of logs was higher for early thinnings than for clearcuts. However, the revenue remained higher than for chips, making the separate harvesting of logs and chips more cost effective than chipping whole trees. In the latter case, an excavator, a forwarder, and a roadside chipper were more cost effective than a harvester, a tractor with trailer, and a mobile chipper, respectively. Harvest of additional woody biomass required limited energy input compared with processing and intercontinental transportation of wood pellets. However, at present, we find very small profits from local additional biomass harvests. The low and fragmented forest cover and important sustainability issues further impede the development of a viable production sector in this region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".