Identification of Factors Affecting Food Productivity Improvement in Kalimantan Using Nonparametric Spatial Regression Method
Bibliographic record
Abstract
Problems of Food Productivity in Kalimantan is experiencing instability. Every year, various problems and inhibiting factors that cause the independence of food production in Kalimantan are suffering a setback. The food problems in Kalimantan requires a solution, therefore this study aims to analyze the factors that influence the increase of productivity and production of food crops in Kalimantan using Spatial Statistics Analysis. The method used is Nonparametric Spatial Regression with Geographic Weighting. Sources of research data used are secondary data and primary data obtained from the Ministry of Agriculture and the Central Statistics Agency. The total area used is 56 regencies/cities in Kalimantan. The results show that there are 13 factors affect food productivity in Kalimantan. These factors are the number of agricultural business households, the number of workers in agriculture, the amount of rice production, rice field area, rice field harvest area, irrigation network area, area of each region, total area based on altitude class, area based on slope/slope class, economic growth rate, Gross Regional Domestic Product, Regional Development Index and Population Density. The best model is obtained using the geographical weighting of the Gaussian kernel function with the cross-validation value 5,65. The criteria for the goodness of the model with the number of knots 3 and order m = 1 have R2 value of 97,19% and the value of AIC is 2,43.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".