Economic wood supply from alternative silvicultural systems : a case study in Ontario's boreal forest
Bibliographic record
Abstract
A modified version of the Harvest Schedule Generator model (HSG) was used to \npredict the economic wood supply from alternative silvicultural systems on a case study \nforest (Seine River Forest) In northwestern Ontario?s boreal forest. Alternative \nsilvicultural systems were compared with traditional clearcut harvesting to determine \nthe impacts on sustainable harvest levels, wood costs and residual timber value. \nResults show large reductions in harvest volumes, increased harvest area and \ndecreased profit for alternative silvicultural systems. Alternative silvicultural systems? \nsavings in regeneration costs did not offset the increased harvest and delivery costs \nnor the reduced volume productivity from the forest as a whole. The different \nsilvicultural systems resulted in little variation in the residual forest age-class structure \nafter 200 years when harvest levels were equal. Based on the assumptions used in \nthis study, the use of alternative silvicultural systems as a replacement for clearcutting \nin northwestern Ontario?s boreal forest would produce undesirable socio-economic \nimpacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".