The effect of leader damage on white spruce (<i>Picea glauca</i>) site tree height growth and site index
Bibliographic record
Abstract
Site trees used to estimate site index are selected based on characteristics that ensure that the tree reflects the potential productivity of the site. Hidden leader damage can make it difficult to identify site trees. Using these trees as site trees could lead to erroneous estimates of site index and height growth trajectories. One hundred and fifteen white spruce (Picea glauca (Moench) Voss) trees were selected, harvested, and split open to identify hidden damage and to quantify the effect of the damage on height growth and site index. A mixed-effects height growth model based on the Chapman–Richards function was formulated. A height growth modifier was included in the model to estimate the effect of leader damage on height growth. It was found that height growth was reduced by 28% in the year that the damage occurred and by 6% and 3% in the following two years. This results in a reduction of about 0.16 m in site index per incidence of damage on average, although this will depend on the age when the damage occurred and the timing between damage events. Since the damage is not outwardly visible, this creates problems when developing and applying site index models.
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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 | 0.000 | 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".