Predicting future diameter of sugar maple in uneven-aged stands of west-central New Brunswick and New York
Bibliographic record
Abstract
We used data from uneven-aged research plots in west-central New Brunswick to validate a sugar maple (Acer saccharum Marsh.) diameter growth model initially developed from remeasured trees in single-tree selection system stands at two New York State locations. We also refitted the coefficients to fit the New Brunswick data and added variables to account for variation across locations and treatments among the New Brunswick plots. The original equation predicted future diameter for New Brunswick trees reasonably well, but a version with coefficients specific to New Brunswick proved more accurate. Adding variables that account for unique features of the New Brunswick data reinforced the notion that growth rates differ across locations, and also that post-cutting diameter growth varies with the intensity of release. Although the New York model and the general model with refitted coefficients unique to New Brunswick indicated that rates of growth do not change throughout a cutting cycle, equations having a variable to account for location of the New Brunswick research sites showed that growth decreased with time. Test results are presented.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".