Site index in agroforestry systems: age-dependent and age-independent dynamic diameter growth models for<i>Quercus ilex</i>in Iberian open oak woodlands
Bibliographic record
Abstract
Despite Quercus ilex L. being one of the most widespread tree species in the Mediterranean basin, there are no growth models in the literature for this species. In this study, we compare age-dependent and age-independent dynamic diameter growth models and discuss the concept of dominance in open stands. A posteriori dominance was determined to fit potential age-dependent growth models and a site index based on diameter growth was defined. Formulations derived from power decline base models (Korf and Hossfeld) best described diameter growth. The best approach for age-dependent models was a polymorphic and with variable asymptotes generalized algebraic difference approach formulation. Residual errors in trees between 20 and 55 cm ranged from ~7.0% in potential growth models to ~15% in age-independent models expanded by density. Using a unique age-dependent dynamic equation for all trees, regardless of dominance, did not increase the error very much. In age-independent models, the inclusion of the defined site index reduced the prediction error but requires that the age of trees is estimated to determine the site index. The difficulty of estimating Q. ilex age makes age-independent models very attractive for system modelling. Age-independent models could be useful in other ecosystems where age estimation is problematic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".