Evaluation of a large-scale forest scenario model in heterogeneous forests: a case study for Switzerland
Bibliographic record
Abstract
Large-scale forest scenario models are widely used to simulate the development of forests and to compare the carbon balance estimates of different countries. However, as site variability in the application area often exceeds the variability in the calibration area, model validation is important. The aim of this study was to evaluate the European Forest Information Scenario model (EFISCEN). As Switzerland exhibits high spatial and climatic diversity, it was taken as a case study. The model output was compared to measured data in terms of initialization, estimation of growing stock, stand age, increment, management, and natural mortality. Comparisons were done at the country level, but also for regions and site classes. The results showed that the initialization procedure of EFISCEN works well for Switzerland. Moreover, EFISCEN accurately estimated the observed growing stock at the country level. On a regional level, major differences occurred. In particular, distribution of the harvesting amounts, mortality, and age-class distribution deviated considerably from empirical values. For future model applications, we therefore propose to define the required harvesting level not per country, but to specify it for smaller regions. Moreover, the EFISCEN simulations should be improved by refining the mortality function and by incorporating more flexibility in forest management practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".