Big Data Review of the Influence of Agricultural Sector Development on Economic Resilience
Bibliographic record
Abstract
The agricultural sector in the East Java's GDP structure has been stable at around 11% over the past 5 years, despite disruptions caused by the Covid pandemic. The stable development of the agricultural sector has contributed to the region's increased resilience. This study aims to identify the relationship between factors influencing agricultural development and resilience in East Java and to formulate related development strategies. The method used in this study is Correlation and Regression. Regional resilience is viewed as the output of changes in the rate of GDP growth. Factors in agricultural development are viewed from Agricultural Production, Land Factors using Big data, including LST, NDVI, and NDWI, Internet User Farmers and Farmer Populations. The results of the study indicate that significant influential factors in agricultural development are found in NDVI, NDWI, and Farmer Population. The study shows that for East Java, development strategies through digital farming have not yet been able to increase regional resilience, and conventional agricultural development methods still dominate.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".