A Light-Adjusted Growth Intercept Model for Predicting White Spruce Site Index
Why this work is in the frame
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Bibliographic record
Abstract
Abstract One of the challenges of managing spruce-aspen mixedwoods in the boreal forest is the difficulty in determining site index for spruce in those stands in which it occurs primarily in the understory. A study was conducted to determine if growth increment of understory spruce, adjusted for available light, could predict spruce site index. In each of nine stands, spruce site index was determined for three dominant trees. Available light (measured as proportion transmitted) and growth increment (estimated by the growth intercept method) were recorded for three to eight understory spruce per stand. Growth increment adjusted for available light predicted spruce site index better (R2 = 0.56 or 0.63, depending on the model used) than unadjusted growth increment (R2 = 0.10), thus indicating the potential for using light-adjusted growth increment to predict site index.
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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