The Prediction of Population Dynamics Based on the Spatial Distribution Pattern of Brown Planthopper (Nilaparvata lugen Stal.) Using Exponential Smoothing – Local Spatial Statistics
Bibliographic record
Abstract
This study aims to predict the population dynamics of Brown Planthopper (BPH) in highly endemic areas of Central Java province, Indonesia. The research was conducted by modifying the method proposed by Legendre and Fortin (1989), through three stages. Those were predicting BPH attacks using Exponential Smoothing Holt Winter, analyzing spatial structure using I, C and Z test on Local Statistic, and making the connectivity inter the periodic predictions of planting season. The results showed that, the studied areas will experience the hotspots phenomenon based on the analysis by the method of Moran's I, Geary's C and Getis Ord Statistic. The analysis of Local Moran's and Getis Ord showed that, four counties namely Boyolali, Klaten, Karanganyar and Sragen experienced a local migration current from region to region around them, whereas other counties are independent. The migration current was influenced by topography, biotic interactions, and anthropogenic factor. Viewed from the spatial scalability in the studied areas, there are four categories of BPH population distribution; point, site, local, and landscape. BPH local migration interregion happened in the County of Klaten, Boyolali, Karanganyar and Sragen. It was caused by some factors: (1) the local climate, (2) the repetition of the use of rice plant variety in a long time, (3) the use of insecticide intensively (3-4 times in one planting period/season), and (4) the irrigation, allowing the spread of BPH larvae and eggs into its surroundings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".