Growth intercept models for black spruce, jack pine and balsam fir in Quebec
Bibliographic record
Abstract
The objective of this study was to develop variable growth intercept models for coniferous species of major importance in Quebec using Nigh's (1997a) modelling technique. Eighty-three, 68, and 70 stem analysis plots of black spruce (Picea mariana [Mill.] BSP), jack pine (Pinus banksiana Lamb.) and balsam fir (Abies balsamea (L.) Mill) were used, respectively. The growth intercept models for black spruce were the most precise, followed by those for jack pine and finally by those for balsam fir, based on the root mean square errors. Results indicated that the accuracy of the models was good, relative to those previously published for other species in Canada. Interim testing of the models revealed a low mean error for all three species that may not be of practical significance for site index determination, although more data should be obtained to further test the models. Key words: balsam fir, black spruce, growth intercept, jack pine, model, nonlinear regression, site index
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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".