Using multivariate factorial kriging for multiscale ordination: a case study
Bibliographic record
Abstract
Vegetation ordination is usually based on classical data reduction techniques such as principal components analysis, correspondence analysis, or multidimensional scaling. The usual methods do not account for multiscale correlations among species. In this paper, we use a geostatistical method, known as multivariate factorial kriging, for studying multiple-scale correlations. The case study was carried out in a mixed broadleaf forest of central Spain. Six tree species were included in the analysis. Data analysis included (i) experimental variogram calculation and modeling with the use of the linear model of coregionalization, (ii) principal components analysis, and (iii) cokriging. The results indicate that correlations among species are different depending on the spatial scale. We conclude that competition for light is the main factor controlling the spatial distribution of species at the plot-level scale of variation. At larger scales of variation, soil conditions and (or) human intervention are the key factors in determining the observed vegetation pattern. Based on the factor scores for the largest scale of variation, we conducted a cluster analysis to identify plots with similar characteristics. The resulting clusters have the remarkable property of being spatially continuous.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".