Comparison of Soil Testing Methods for Plant Available Phosphate
Bibliographic record
Abstract
Most test methods for plant available soil phosphate are based on the extraction with a chemical solution. The objective of this study is to evaluate available phosphate of various tests at different soil phosphate levels. Two experiments were conducted as follows: i) Extracting capacities of soil phosphate tests - Mehlich III, Mehlich II, Bray I, Olsen, Kelowna, and Modified Lancaster(Mod. Lancaster) - were compared with that of Lancaster test for the soils collected from 32 paddy and 27 upland fields with various soil chemical properties. ii) Field trials on comparing to phosphate uptake by plant were accomplished by cultivating rice and corn plants in the pots filled with the soils. Available phosphate of Lancaster test was significantly correlated with those of Mehlich III, Mehlich II, Bray I, Olsen, Kelowna, and Mod. Lancaster. In upland soils, available phosphates of all the tests were curvilinearly regressed with phosphate uptake by corn. The determination coefficients ( $R^2$ ) of the regression equation between available phosphate in soils and phosphate uptake by plants were ranged from 0.861 (Mehlich III) to 0.741 (Olsen). In paddy soils, the available phosphate measured by Mehlich III and Lancaster was significantly correlated with phosphate uptake by rice. In conclusion, Lancaster and Mehlich III tests could be used for predicting available phosphate in upland and paddy soils.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".