Estimating site quality from early height growth of white spruce and red pine plantations in the Thunder Bay area / James S. Thrower. --
Bibliographic record
Abstract
Growth intercepts and breast height-age height growth curves were developed for estimating the site quality using early height growth in white spruce {Picea glauca (Moench) Voss) and red pine (Pinus resinosa Ait.) plantations in the Thunder Bay, Ontario area. These \nmethods for estimating site quality were developed from height growth data obtained using annual node measurements and stem analyses of three dominant, undamaged, trees in each of 46 white spruce and 25 red pine plots located throughout the Thunder Bay area. \nWhite spruce growth intercepts were computed using series of one through seven internodes from eight starting heights between 0 and 3.0 m. Red pine growth intercepts were computed using series of one through 10 internodes from the same eight starting heights. The best estimates of white spruce and red pine site quality were obtained from the average length \nof the first three, four, and five internodes above 2.0 m, and the first three, four, and five internodes above 1.5 m, respectively. \nBoth white spruce and red pine height growth patterns were best described by an expanded Chapman-Richards function capable of expressing polymorphic height growth patterns. These height growth patterns compared well with those of eastern Ontario and the Lake States. Height growth below breast height for both species was very erratic and was not \nrelated to site quality. Consequently, total height-age height growth curves that included this \nearly erratic height growth did not provide accurate estimates of site quality in these white spruce and red pine plantations. Growth intercepts provided accurate estimates of site quality in early years. However, breast height-age height growth curves provided more accurate estimates of site quality when plantations exceeded the ages required for these growth intercepts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".