Assessment of chemical extractants to predict available potassium for rice in calcareous soils.
Bibliographic record
Abstract
No reliable soil test is currently available to assess the potassium (K) status of calcareous soils of Iran. Therefore, the relative efficiency of 10 extractants to predict potentially available K to lowland rice (Oryza sativa L.) was evaluated in 27 calcareous soils. The extracting capacity of the extractants was quite different, being highest for HNO 3 and lowest for CaCl 2 . The amounts of K extracted were highly correlated among chemical soil tests; the only exception was a non-significant relationship between HNO 3 -K and CaCl 2 -K. The variability in the soil K displaced by the extractants was best explained by cation exchange capacity (CEC) and silt content followed by calcium carbonate equivalent (CCE). The coefficients of determination among extractable K and rice shoot dry weight and K uptake were significant. However, K extracted by each soil test was more closely correlated with plant K uptake than with dry weight. The highest correlation was observed among K uptake and Sr-citrate-K and modified Kelowna-K, and the lowest with HNO 3 -K. Inclusion of soil properties improved the prediction power of plant-available K. Furthermore, the silt content had the most influential role in the prediction capability of K soil test. The results show that modified Kelowna, Sr-citrate and DTPA-NH 4 HCO 3 are appropriate extractants to estimate potentially available K for paddy rice in highly calcareous soils of Iran. However, relatively high variability in soil characteristics justifies an urgent need for more correlation and field calibration studies to improve K availability indices.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".