Combining a predicted diameter distribution with an estimate based on a small sample of diameters
Bibliographic record
Abstract
The diameter distribution of a forest stand is of great interest in many situations, including forest management planning and the related prediction of growth and yield. The estimation of the diameter distribution may be based on, for example, a measured sample of diameters or the application of previously estimated parameter prediction models (PPMs), which relate the parameters of an assumed distribution function to some stand characteristics. We propose combining these two information sources. The approach is adopted from the mixed-effects modelling theory. The PPMs are treated as mixed-effects models, the residuals being stand effects. These stand effects are predicted using a small sample of tree diameters with the best linear predictor. A study conducted with a Spanish pine data set showed that in a situation where the predictors of the PPM include errrors, the prediction can be improved even by using a sample plot of as few as five sample trees. Vice versa, a distribution based on a sample plot of 3–15 sample trees can be significantly improved by utilizing existing PPMs. An additional simulation study was conducted to further investigate how the violation of different underlying assumptions of the method affects the performance.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".