Relationship between Soil Salinity and Physico-chemical Properties of Paddy Field Soils of Jhilwanja Union, Cox’s Bazar, Bangladesh
Bibliographic record
Abstract
This study attempts to examine the relationship between soil salinity and physico-chemical properties of paddy field soils of Jhilwanja union in Cox’s Bazar, Bangladesh. Data were analyzed by the help of Statistical Package for the Social Sciences (SPSS: version-18) and study area map was prepared by Arc GIS 9.3 software. Present investigation reveals that soil textural class ranged from sandy to loam, a mean bulk density 1.58 g/cm3, and high soil temperature (M = 31.55). Soils were moderately acidic to basic and soil salinity (EC) content comes under low to medium (minimum = 3.10 dS/m, maximum = 7. 24 dS/m) range. The organic matter level exhibited absolutely lower (M = 0.75) than good agricultural soil. Soils of the area represents low in total N (M = 0.08), very low in total P content (M = 0.06), exchangeable Ca (M = 0.0009) were too low and lower content of exchangeable Mg (M = 0.26), but high level of exchangeable K (M = 0.57) content. Simple regression analysis showed that there was a significant effect of soil salinity on soil pH, soil temperature, OM, total N, total P, exchangeable K and Mg. Repairing coastal embankment (rubber dam), cultivating native high yielding variety, using organic fertilizer, implementing Integrated Soil Nutrient Management (ISNM) and adapting Integrated Coastal Zone Management (ICZM) can increase crop production and soil fertility level in study area.
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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) | 0.001 | 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".