Height growth and site index of trembling aspen in north central Ontario / by Kerry C. Deschamps
Bibliographic record
Abstract
Height-growth and site-index curves were developed for estimating site \nquality of trembling aspen {Populus tremuloides Michx.) in north central Ontario. \nThese curves were developed from stem analysis data of dominant and \ncodominant, uninjured aspen trees obtained from 89 plots covering a wide range \nof site quality in north central Ontario. The actual height-growth patterns were \nmodelled using several non-linear biological growth models: Chapman - \nRichards function, modified Weibull function, Monserud logistic function and an \nexpansion of the Chapman - Richard function. In addition, a new height-growth \nmodel was developed using a similar approach to that of Cieszewski and Bella. \nHeight-growth patterns of aspen varied with level of site-index. Height \ngrowth curves show an almost linear growth pattern for poor sites (SI < 16 m) \nto a highly curvilinear pattern on good sites (SI > 24 m). Medium sites (S116-24 \nm) show a rapid linear surge of height growth before 40 years followed by a \nslowing curvilinear pattern. \nHeight-growth curves, site-index curves and a site-index prediction \nequation were constructed from trembling aspen stem analysis data. Goodness \nof fit tests were computed using a modified Chi-square test. In addition, the \naccuracy of the height-growth curves, site-index curves and site-index prediction \nequation were tested using independent stem analysis data from 19 plots \nsupplied by the Ontario Ministry of Natural Resources. Comparisons with the \nindependent data source shows close agreement; the 95% site index error \nprediction interval for the site-index curves and the site-index prediction equation \nare 0.19 ? 1.37 and 0.21 ? 1.35 respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".