Validation of empirical yield curves for natural-origin stands in boreal Ontario
Bibliographic record
Abstract
In Ontario, yield tables for forest management planning have remained relatively unchanged since initial work in the 1950s that was based on a limited number of temporary sample plots. In 2000, the Forestry Research Partnership accelerated work on the Benchmark Yield Curve Project (initiated several years earlier by the Ontario Ministry of Natural Resources, OMNR) to update these tables. The resulting yield curves incorporated data from more than 3000 permanent sample plots (PSPs) maintained in Ontario as well as PSPs from neighbouring and ecologically similar jurisdictions. Two stratifications were considered: OMNR’s Northeast Region standard forest units and leading species. The 10 forest units considered cover the major commercial species in the boreal forest in Ontario. Equations were fit to the data to predict the growth and yield by stratum. The equations were validated against independently collected data and compared to predictions from the current wood supply yield curves in Ontario: Plonski’s yield tables, modified Plonski, and northeast regional curves. Results of the validation showed that, with the exception of the MW2 and SF1 forest units, the new yield curves generally had less bias for gross total volume than Plonski and modified Plonski. Results for net merchantable volume were consistent with those for gross merchantable volume. The MW2 and SF1 forest units are more mixed in terms of species type, species light tolerance, and age. A leading species approach resulted in better predictions and is recommended for these forest units. Key words: wood supply, benchmark yield curves, mixedwood yield, yield model, Forestry Research Partnership
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".