Evaluation of Soil Retention Capabilities Using RETC Application in Various Paddy Field Management Systems in Purwantoro District, Indonesia
Bibliographic record
Abstract
Prolonged dry seasons are one of the most influential impacts of global warming on the agricultural sector.Soil water retention is the ability of soil to hold water, which is presented in the form of pF curves (graphical representations of the relationship between soil water content and soil water potential).The soil's physical properties also influence the soil's water retention ability, which affects crop growth, crop yield, and land productivity.This study aims to measure soil water retention using the RETC program on various paddy field management systems: organic, semi-organic, and conventional.This research used a survey method in rice fields with different management systems and soil physical indicators approaches.The RETC program can be used to measure soil water retention in paddy fields accurately and efficiently.The results showed that the paddy field management system in Purwantoro Sub-district affects the soil's ability to retain water.The organic rice field management system has the highest available water content of 18%.The increase in soil water retention has a very significant positive correlation with soil organic matter content, where the organic matter content in the organic rice field management system is higher than the semi-organic and conventional management systems by 4.33%.Soil water retention capacity was determined by soil fraction content (0.522**), dust content (-0.438**), and effective depth (0.663**).The higher the clay content, the higher the available water content.
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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 | 0.000 |
| 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 | 0.001 | 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".