Developing a general method for the estimation of the fertility rating parameter of the 3-PG model: application in <i>Eucalyptus globulus</i> plantations in northwestern Spain
Bibliographic record
Abstract
Simple, operational tools are required for forest managers to quantify the effects of soil fertility on tree growth and ecosystem sustainability leading to precise, sustainable forest management. The simplified process-based 3-PG model (Landsberg, J.J., and Waring, R.H., For. Ecol. Manage. 95: 209–228, 1997) provides a useful framework for operational prediction of forest growth. However, no simple objective method for relating the effects of available soil nutrients to the model fertility parameter fertility rating (FR) is yet available. The present study aimed to compare several general modeling approaches for the estimation of FR values from soil relative nutrient contents (RNCs) to maximum nonlimiting values, measured in the whole soil profile, at continuous inventory plots of Eucalyptus globulus Labill. in several locations under different parent materials in northwestern Spain. The modeling approaches tested provided good predictions of FR values from RNCs. In particular, using the minimum value of the most significant RNCs showed considerable potential for modeling FR values and plantation growth responses to them. This modeling approach showed promise to be further tested as a generally applicable strategy for estimating the effect of soil nutrients on forest plantations growth.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".