A forest-level genetic algorithm based control system for generating stand-specific log demand distributions
Bibliographic record
Abstract
This study was established to test whether the fit between the overall log demand distributions required by mills and the cumulative log output distributions could be improved by localizing the demand matrices controlling the bucking-to-order process on modern cut-to-length harvesters. Fifteen mature Norway spruce (Picea abies (L.) Karst.) stands were cut with a bucking simulator under the control of both the stand-specific and uncontrolled reference demand matrices. The test simulations involved three Norway spruce log products: sawlogs, veneer logs, and pulpwood logs. The stand-specific demand matrices for these products were generated in parallel using a genetic algorithm (GA) based search system initiated by the taper of each tree to be harvested and the overall demand and price matrices of each log product. The GA system was run with both the real stem data (i.e., stem profiles recorded by harvester) and the stem data compiled from preharvest forest inventory data. The reference matrices were the overall demand matrices adopted from two Finnish sawmilling companies. The test results showed that compared to the uncontrolled reference matrices the GA-controlled demand matrices produced a 22%103% higher total fit between the overall log demand distributions and the cumulative log output distributions at the forest level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".