Modelling survival probability of individual trees in Norway spruce stands under different thinning regimes
Bibliographic record
Abstract
The probability that an individual tree will remain in even-aged Norway spruce (Picea abies (L.) Karst.) stands subjected to different thinning programmes was modelled, using data from a thinning experiment established in 25 localities in southern Sweden. A logistic regression approach was used to predict the probability and the HosmerLemeshow goodness-of-fit test to evaluate the fit. Diameter at breast height (DBH), quadratic mean DBH, thinning intensity, thinning quotient, basal area, number of stems per hectare, stand age, number of thinnings, and site index were used as explanatory variables. Separate analyses for stands thinned from below, stands thinned from above, and unthinned stands were performed. The modelled probability graphs for trees not being removed, plotted against their diameter at breast height, had clear S-shapes for both unthinned stands and stands thinned from below. The graph for stands thinned from above was bell-shaped.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".