Bibliographic record
Abstract
Wood supply of the major industrial species groups (spruce–pine–fir [Picea–Pinus–Abies spp.] and poplar [Populus spp.]) in the boreal forest of Ontario is forecast to fall below demand in the relatively near future. This has lead to more interest in the growth and yield of mixedwood forests. Mixedwood stands are defined for forest management planning as stands in which 26% to 75% of the canopy is softwood. With an average growth rate one-third higher than the average for all forest types combined, mixed species stands have potential to mitigate some of the shortfalls. This paper reviews the history of yield curve development in Ontario and some of the current initiatives in mixedwood modeling. The Forestry Research Partnership, a partnership between Tembec, the Ontario Ministry of Natural Resources, the Canadian Forest Service, and the Canadian Ecology Centre, was formed in 1999. One of the first projects of the Partnership was to update the provincial yield curves. These updated curves provide good estimates of yield for mixedwoods on upland, drier sites but mixedwoods on moister sites need to be further stratified by leading species. Mid-rotation activities such as density regulation and partial harvesting in the selection or shelterwood silvicultural systems are generally tree-level activities. These are more compatible with tree-level models. Ontario is calibrating the Forest Vegetation Simulator (FVS) for use in Ontario and this shows particular promise in mixedwood modeling. Key words: mixedwood growth, yield tables, FVS
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".