Site-Index Curves and Growth Intercepts for Young White Spruce Plantations in North Central Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Site-index (heightߝgrowth) curves, site-index prediction equations, and growth intercepts were developed from internode measurements and stem-analysis data using dominant trees in 69 plots located in white spruce plantations aged 19 to 32 years total age. Site-index curves were based on breast-height (1.3 m) age because our analyses show that height growth below breast height is slow and erratic and is poorly related to site index (dominant height at 15 years breast-height age). The most precise model for computing heightߝgrowth curves was a Newnham constrained polymorphic expression (Newnham, R.M. 1988. A modification of the Ek-Payandeh nonlinear regression model for site-index curves. Can. J. For. Res. 18:115ߝ120) of the Ek nonlinear regression model (Ek, A.R. 1971. A formula for white spruce site-index curves. University of Wisconsin For. Res. Note 161. 2 p). Comparisons showed that site-index curves in North Central Ontario were comparable to site-index curves for white spruce plantations in southeastern Ontario. The first three to five internodes above 2.0 m gave the most precise estimates of site index based on growth intercepts. North. J. Appl. For. 23(4):257–263.
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.
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 it