The Assessment of Soil Fertility Index for Evaluation of Rice Production in Karanganyar Regency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rice (Oryza sativa L.) is a very important food crop because the result is used as a staple food for residents in Indonesia. Higher food fulfillment leads to the increase of rice production of the Mojogedang sub-district. Paddy fields that have high soil fertility will produce good rice productivity. Rice fields in Mojogedang Sub-district are managed with organic systems and conventional systems, the management of different fields of rice field certainly affects the level of fertility in the paddy fields so it is necessary to evaluate the soil fertility index. The survey area consists of 10 points with organic and conventional management systems. The parameters taken include chemical and biological properties of soil, including; pH, redox potential, C-organic, CEC, base saturation, P available, available K, N Total, C/N ratio, and total microbial. The data obtained by performed analysis of the main component principal component analysis (PCA) using statistical applications. Then after complete the calculation of The Soil Fertility Index (SFI) at each point and management system. The results of statistical analysis obtained soil Fertility Index on organic management systems have a class of 4 or very high and in conventional management systems have a class of 3 or High. The value of the index obtained is strongly influenced by the K indicator available where the indicator has a noticeable effect on the various management systems. Increased soil fertility index due to the use of manure that can improve plant nutrients and applied for long periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it