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 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) | 0.000 | 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".