On the analysis of cubic smoothing spline-based stem curve prediction for forest harvesters
Bibliographic record
Abstract
In the cut-to-length (CTL) harvesting system the felling, delimbing, and bucking processes take place at the harvesting site. The optimal cutting points along the stem can be determined if the whole stem curve is known. In practice, however, it is not economically feasible to measure the whole stem first before crosscutting, and hence the first cutting decisions are usually made when only a short part of the stem is known. Predictions are used to determine the cutting pattern to compensate for the unknown part of the stem. In this paper our interest focuses on stem curve prediction in a harvesting situation and we study a modified version of a cubic smoothing spline-based prediction method devised by Nummi and Mottonen (T. Nummi and J. Mottonen. 2004. J. Appl. Stat. 31: 105–114). The method's performance was assessed in five different final felling stands of spruce and pine, collected by harvesters in southern Finland. The results for the spline approach are very promising and show the superiority of the method over the linear mixed-model-based approach of Liski and Nummi (E. Liski and T. Nummi. 1995. Scand. J. Stat. 22: 255–269) and also over the approach based on the variable-exponent taper equation of Kozak (A. Kozak. 1988. Can. J. For. Res. 18: 1363–1368).
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".