Assessment of white spruce and jack pine stem curvature from a nelder spacing experiment.
Bibliographic record
Abstract
This study presents a method for calculating stem curvature for trees with multiple deviations.Generally, tree curvature is assessed using the maximum deflection method.It consists of measuring the farthest point from a straight line drawn between the large and small ends of a stem.It works fairly well for a single deviation but gives poorer results for stems with several deviations.The stems used for developing this method were harvested from a 32-yr-old Nelder spacing experiment established near Woodstock, New Brunswick, Canada.A total of 96 trees were selected for this study from the white spruce (Picea glauca [Moench] Voss) and jack pine (Pinus banksiana Lamb.) that were planted on the same Nelder circle.This particular plantation design offered a gradient of initial spacings ranging from 640 to 12,000 stems/ha.Results of analysis revealed that initial spacing had an impact on tree curvature.Stem curvature increased with wider initial spacing.However, this influence varied between species and differed according to the method used to calculate curvature.The vector length calculation method showed that stem curvature in jack pine was more pronounced and more often encountered at lower densities than in white spruce.It was also observed that tree shape was influenced by the cardinal points with white spruce growing more in westerly and southerly directions.
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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 | 0.000 |
| 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) | 0.001 | 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".