Polymorphic site index curves for black spruce and trembling aspen in northwest Ontario
Bibliographic record
Abstract
Site index curves were developed using stem-analysis data from dominant and codominant trees in 230 black spruce and 194 trembling aspen plots located in mature, well-stocked, even-aged stands. Black spruce plots were in five separate and independent data sets located on mineral and organic soils; trembling aspen plots were in three separate and independent data sets located on mineral soils. Computations showed that the Newnham nonlinear regression model was slightly more precise than other models commonly used for developing site index curves. This model accurately expresses polymorphic height growth for a wide range of site index and age and constrains predicted height to agree with specified 50-year tree height (site index). Computations showed that height-growth curves were similar for each of the five black spruce data sets; likewise, curves for each of the three trembling aspen data sets were similar. Therefore, the five black spruce and the three trembling aspen data sets were combined for computing a single set of black spruce and a single set of trembling aspen site index curves for Northwest Ontario. Comparisons between our Northwest Ontario site index curves and other black spruce and trembling aspen curves showed large differences in height-growth patterns for older ages. Reasons for these differences are uncertain but in many instances these could be artifacts related to study methods involving various amounts and kind of data and variations in analytical techniques. In some cases differences in climate, soil and topography may also be related to variations in these site curves. Key words: site quality evaluation, polymorphic height growth, site index curve comparisons
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".