Height, growth and site index of jack pine (Pinus banksiana Lamb) in the Thunder Bay area : a system of site quality evaluation / Daniel J. Lenthall. --
Bibliographic record
Abstract
Height growth patterns and site index were studied using stem analyses \ntaken from dominant and codominant trees growing on 109 plots located in \nmature, natural, fully stocked evenaged stands of jack pine {Pinus banksiana Lamb.) in the Thunder Bay area. The observed height/age data were modeled using several nonlinear biological growth models: Richards growth model (1959); a modified Weibull function; and an expansion of the Richards model proposed by Ek (1971). \nHeight growth patterns of jack pine varied with level of site index, being \nmore curvilinear as level of site index increased. Height growth patterns were similar for jack pine growing on glacialfluvial sands, on moraines, on lacustrine soils and shallow to bedrock soils. Analyses showed that site index curves were more precise when based on breast height age instead of total age of the trees. \nHeight growth curves, site index curves and a site index prediction equation \nwere calculated from the jack pine stem analyses data. A modification of the \nChi-squared distribution was used for testing the accuracy of the site index \ncurves and prediction equation. The accuracy of the computed curves was tested using independent stem analyses data from 32 additional confirmation plots. Comparisons with this independent data showed very close agreement; the 95% prediction intervals calculated for the site index curves and site index prediction equation using independent data are -0.17 ? 0.89 m and -0.20 ? 1.14 m respectively. \nComparison between Plonski?s (1974) formulated site index curves for jack \npine and the site index curves produced in this study indicate differences in \npredicted heights at ages greater than index age (50 years), but no differences younger than index age. Plonski?s site index curves showed lower predicted heights for each level of site index at ages greater than 60 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".