Soil Fertility Levels in Bangladesh for Rice Cultivation
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
Determination of soil fertility with minimum data set for crop zoning and devising fertilizer recommendations as well as soil fertility evaluation method based on soil properties. The data were collected from existing literatures and scoring was done on 0–100 scale. The lowest score was assigned for the minimum value of tested attributes and then gradually higher scoring values. Arithmetic, weighted, geometric and most minimum of mean scores were calculated and their performances were compared with grain yield of dry season irrigated (Boro) rice. Soil fertility in 10-12 and 39-52% areas in Bangladesh are very low and low, respectively. Medium fertile and fertile soils are distributed in 17-41% and in about 8% areas of the country. About 55% soils scored 70–95 (medium to high SOC) and the rest belongs to inferior quality. In some areas P build up has taken place (25% areas), but widespread K mining. Sulphur and Zn status in about 40% areas are low to very low (scored <35 and <40). Soils of the major areas of the country are with low pH (5.0-6.0) and CEC in the range of 15-25 cmolc kg-1. Weighted mean score and most minimum of eight attributes score showed good relationships with dry season irrigated rice yields than other tested methods indicating that this technique can be used for soil fertility rating in tropical countries.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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