Predicting the future diameter of stems in Norway spruce stands subjected to different thinning regimes
Bibliographic record
Abstract
The future diameter of stems in even-aged Norway spruce (Picea abies (L.) Karst.) stands subjected to different thinning regimes was modelled, using data from a thinning experiment established in 25 localities in southern Sweden. Separate models were developed for stands thinned from below, stands thinned from above, and unthinned stands. A bivariate approach was used to construct the models, based on DBH data from the initial stand and from the same trees at future points in time. The approach entails that the dependency between initial and future DBHs can be directly used to predict the future DBH. Also, the modelling used stand and site characteristics together with information about the stand density management regime. The initial stands were assumed to be unthinned, and the dominant height was assumed to be 1218 m. A logistic function was used to predict which individual trees would remain at future points in time. The mean and standard deviation of the differences between observed and predicted future diameters were used to validate the models. When the prediction period was approximately 33 years, the mean was typically underestimated by 4 mm, and the standard deviation was approximately 40 mm.
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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 | 0.001 |
| 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 | 0.001 | 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".