Developing ecoregion-based height-diameter models and reference-age invariant polymorphic height and site index curves for black spruce and jack pine in Manitoba
Bibliographic record
Abstract
Eight different height-diameter models, five height prediction models with stand variables as predictors and six height and site index models were selected, examined, compared and developed for black spruce (Picea mariana [Mill.] B.S.P.) and jack pine (Pinus banksiana Lamb.) in Manitoba. Eight different height-diameter models were fitted using nonlinear modeling techniques and compared in each of the five ecoregions in Manitoba: Churchill River Upland (Ecoregion 88); Hayes River Upland (Ecoregion 89); Lac Seul Upland (Ecoregion 90); Lake of the Woods (Ecoregion 91); Mid-Boreal Lowland (Ecoregion 148). Results suggested that the Weibull-type and Chapman-Richards models were the most suitable models. Differences of the height-diameter relationship among and between ecoregions were tested. Testing results suggested that height-diameter models significantly differred between ecoregions, indicating ecoregion-based or 'local' height-diameter models are needed for prediction purposes. The ecoregion-based height-diameter models developed in this study may provide more accurate information for developing forest growth and yield models. Five height prediction models were examined with the addition of stand density variables into the base height-diameter model. Adding stand variable resulted in increased prediction accuracy. Six height and site index models were examined and compared for black spruce and jack pine based on the provincial stem analysis data and the most suitable models were selected for Manitoba and the provincial height and site index prediction tables were produced.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".