Developing and testing the applicability of a decision support system for the planning of juvenile jack pine thinning
Bibliographic record
Abstract
An investigation was made into the applicability of a model used to assist in \nplanning the allocation of pre-commercial thinning to juvenile jack pine stands. The \nhypothesis of this study was that planning pre-commercial thinning of juvenile jack pine \nto focus on areas within a stand {e.g. high density areas with the largest potential for \nresponse) is cheaper and more efficient than allocating a "blanket" pre-commercial \nthinning over entire jack pine stands. The model incorporates machine costing models, \npre-commercial thinning productivity estimates, stand density maps, and road networks \nto investigate the potential cost savings of detailed planning of pre-commercial thinning. \nIntegration of a GIS database, remote sensing, and network analysis provided an \nexperimental decision support system (DSS) for planning the allocation of precommercial \nthinning. The DSS was applied to two study areas in northwestern Ontario \ncontaining juvenile jack pine stands. Based on the case study results, there appears to \nbe potential for a total cost savings of 12 to 25 percent by planning and focusing precommercial \nthinning treatments to key areas of a stand. Estimated cost savings were \nreduced as the stem density spatial pattern became more uniform and the average stand \ndensity approached the eligible density for thinning. Cost estimates were also found to \nbe sensitive to the pre-commercial thinning productivity estimates. The planning model \ncould be applied to other problems involving spatial components such as skidder trail \nplanning in harvest blocks. Use of this DSS could assist in investigating the interactions \nbetween stand density patterns, pre-commercial thinning productivity, pre-commercial \nthinning equipment operational costs and allocations pre-commercial thinning treatments \nwithin a forest stand.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".