Impacts of soil, water source, and agro-climatic conditions on exchangeable sodium in rice-cultivated lowland soils of Sri Lanka
Bibliographic record
Abstract
Rice is sensitive to salt stress, commonly caused by high concentrations of sodium (Na) in soils. However, the concentration and spatial variability of exchangeable Na in lowland paddy fields of Sri Lanka are not known. This study examined the interactive effects of the agro-climatic zones (ACZs), soil orders, and water sources on exchangeable Na in lowland paddy fields in Sri Lanka using 8566 soil samples. Exchangeable Na was extracted using 0.01 mol/L CaCl2 solution and detected using inductively coupled plasma-mass spectrophotometry. Exchangeable-Na concentration ranged from 0.05 to 4814 mg kg−1 with a mean concentration of 140.7 mg kg−1, and these values were within the optimal range reported for tropical paddy soils. The distribution of exchangeable Na was right skewed with 44%, 35%, and 9.3% of samples falling into 0–100, 100–200, and 200–300 mg kg−1, respectively. Samples from the Low country Dry zone had the highest (157 mg kg−1) Na concentration while that in the Upcountry Intermediate zone was the lowest (13 mg kg−1) ( p < 0.05). Vertisols recorded the highest (255 mg kg−1) and Ultisols recorded the lowest (81 ± 5.0 mg kg−1) among soil orders. Soils receiving supplementary irrigation in Low country Dry zone had lower Na than that in rainfed systems. Exchangeable-Na concentration was positively correlated with soil pH ( p < 0.05). Even though the concentrations of Na in soil samples were within the optimal range, agronomic decisions based on ACZ, soil order, and water source need to be made to minimize the potential development of saline–sodic soils in the study areas.
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.
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".