Spatial autocorrelation study of fatal yellowing in organic oil palm in the eastern Amazon
Bibliographic record
Abstract
The oil palm (Elaeis guineensis) is a very important source of vegetable oil worldwide. While oil palm cultivation is not currently economically significant in Brazil, there is immense growth potential for this crop in the Amazon region, with no need for deforestation. Fatal yellowing (FY) is a major phytosanitary barrier to the development of oil palm cultivation in the region. Many plantations have been devastated by FY, but its causes are unknown. The objective of this study was to analyze the spatial autocorrelation of FY in the oil palm crop by applying Moran’s Index (I). The experiment was carried out on a large farm (>8000 ha) in the municipality of Acará, in the state of Pará (Brazil), with 139 plots of oil palm of different genetic materials and ages. The number of diseased plants per plot has been recorded since 2001 and used in the analysis. A positive autocorrelation of disease incidence was calculated based on the observations of the current study. By calculation of the Local Moran’s I it was possible to identify the presence of high-incidence regions in the northeast and the central-west areas of the farm and low-incidence areas in the south and the north and in outlying plots. The approach described in this study was useful in identifying regions with the highest occurrence of FY, and could be useful in the management of this disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".