Development of a basal area growth system for maritime pine in northwestern Spain using the generalized algebraic difference approach
Bibliographic record
Abstract
A basal area growth system for single-species, even-aged maritime pine (Pinus pinaster Ait.) stands in Galicia (northwestern Spain) was developed from data of 212 plots measured between one and four times. Six dynamic equations were considered for analysis, and both numerical and graphical methods were used to compare alternative models. The double cross-validation approach was used to assess the predictive ability of the models. The data were best described by a dynamic equation derived from the Korf growth function using the generalized algebraic difference approach (GADA) by considering two parameters to be site specific. The equation was fitted in one stage using the base-age-invariant dummy variables method. In addition, the system incorporates a function for predicting initial stand basal area, in which the site-related variable was expressed as a power function of site index. This function can be used to establish the starting point for the projection equation when no inventory data are available. The two equations are compatible. The effect of thinning on basal area growth was examined; the results showed that there was no need to use a different equation to reliably predict postthinning basal area development. The nonlinear extra sum of squares method indicated differences in the model parameters for the two ecoregions (coastal and interior) defined for this species in the area of study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".