Effect of decision variable definition and data aggregation on a search process applied to a single-tree simulator
Bibliographic record
Abstract
This paper focuses on how computer execution times and net present value (NPV) are affected by different groupings of tree-selection harvest controls, different procedures to determine harvest timing, and tree data aggregation. The problems related to stand management are viewed as a hierarchy, where the main problem is determining harvest periods and the subordinate problem is determining what trees to cut in a given set of harvest periods. The solution technique is a derivative-free search process, and the objective is to maximize the NPV of harvest revenues for a stand over a given planning horizon. The tree-selection harvest controls are based on diameter and species groupings. The procedure to determine harvest timing is based on Tabu search and fixed cutting cycles, respectively. Sensitivity analysis is performed for a selection of stands in southern Sweden, where each stand is represented by a set of inventoried plots. Both even-aged and uneven-aged management are considered. Solutions improved with the number of decision variables. The Tabu search procedure proved very efficient at determining harvest periods for the even-aged problems. For the uneven-aged problems, fixed cutting cycles approximated the harvest timing problem, but at considerably shorter execution times. It is suggested that aggregated data be used for determining harvest timing, after which, using the original nonaggregated data, the tree-selection problem for a given set of harvest periods can be resolved.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".