Patterns between crown characteristics and radial increment in trees are similar during recovery and normal growth: a long-term example from old-growth forests
Bibliographic record
Abstract
Crown traits and competition attributes have an important effect on tree radial increment. Relationships among these elements are modeled using the distributions of the crown characteristics in a given calendar year, but these patterns can differ over time. The suitability of the patterns during recovery and normal growth was investigated using silver fir, Abies alba Mill., in old-growth forests. Generalized additive models (GAMs) for silver fir in the older (OG, trees aged 136–300 years) and younger (YG, trees aged 45–135 years) generations were developed. To test the validity of these GAMs, field data sets representing silver fir recovery and normal growth were used. For silver fir in OG, crown transparency had the largest effect on tree growth, explaining more than 25% of the variance. For silver fir in YG, relative crown length had the largest effect on tree growth, explaining more than 15% of the variance. The absolute relative prediction errors, AREmin and AREmax, were less than 0.03 and 1.50 mm, respectively. The developed GAMs are suitable during recovery and normal growth, but the GAMs were fitted to a relatively small area, neglecting climatic gradients and different disturbance types. This type of investigation should be continued on a larger scale.
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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".