Statistical models to identify stand development stages by means of stand characteristics
Bibliographic record
Abstract
Stand development stages differ mainly in terms of stand structure, stand density, and mortality patterns. As the fulfilment of socio-economic forest functions often depends on stand structure and density, knowledge of the frequency and distribution of stand development stages is needed for optimal forest management. Development stages have been previously identified only qualitatively by experts in forest ecology, but this study developed and compared statistical models to identify development stages by means of stand characteristics. Data from the Austrian National Forest Inventory with 4761 observations of stand development stages were used as the training data set for quadratic discriminant analysis and multinomial logistic regression. The models differ only marginally in terms of the hit ratio and the overall kappa statistic (both determined by means of an independent test data set). The quadratic discriminant analysis has the advantage that the user can reduce or even avoid the influence of the group size on the group-specific model performance by using equal prior probabilities. Furthermore, the discriminant analysis showed the best model behaviour in terms of the explanatory variables and performed best in identifying the stages that were infrequent in the training data set.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".