Incorporating environmental and geographical information in forest data analysis: a new fitting approach for universal kriging
Bibliographic record
Abstract
Universal kriging gives the optimal linear model to incorporate auxiliary information in data analysis in the presence of spatial dependence of observations if the underlying variogram is known. However, in practice, the variogram is typically unknown and its estimation constitutes one of the major problems in universal kriging theory. In this paper, a new method is proposed to estimate the variogram and the mean function in universal kriging based on the relationship between the second moments of the variable Z(s) and the auxiliary variables. The performance of the proposed method is analysed in three case studies: the prediction of site index in an Italian stone pine ( Pinus pinea L.) forest, the estimation of growing stock in a Scots pine ( Pinus sylvestris L.) stand, and the assessment of the environmental factors involved in the distribution of a Meliosma species in a tropical montane cloud forest. The results show that the proposed method performs as well as the maximum likelihood and least squares methods in terms of unbiasedness and precision of the kriging predictor and prediction error variance estimation. The proposed method allows the spatial variability linked to environmental and geographical factors to be identified in the analysis of data from forest ecosystems.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".