Simultaneous Analysis of Soil Macronutrients Using Ion‐Selective Electrodes
Bibliographic record
Abstract
Automated sensing of soil macronutrients would be useful in mapping soil nutrient variability for variable‐rate nutrient management. Ion‐selective electrodes (ISEs) are a promising approach because of their small size, rapid response, and ability to directly measure the analyte. This study reports on the laboratory evaluation of a sensor array including three different ISEs, based on TDDA–NPOE and valinomycin–DOS membranes, and Co rod, for the simultaneous determination of NO 3 –N, available K, and available P in soil extracts. Thirty‐seven Illinois and Missouri soils were extracted using the Kelowna soil extractant (0.25 mol L −1 CH 3 COOH + 0.015 mol L −1 NH 4 F). The response of each electrode type in mixed solutions of NO 3 , K, and P ions was modeled based on the Nikolskii–Eisenman equation with all coefficients of determination ( r 2 ) ≥0.95 ( P < 0.001). In soil extracts, the NO 3 ISEs provided concentrations similar to those obtained with standard laboratory methods ( r 2 = 0.89, P < 0.001). Concentrations obtained with the K ISEs were about 50% lower than those obtained with standard methods due to lower K extraction by the Kelowna solution ( r 2 = 0.85, P < 0.001). The P ISEs provided concentrations about 64% lower than those obtained with standard methods due to a combination of decreased P estimates in soil extracts and lower P extraction by the Kelowna solution; however, there was a strong linear relationship ( r 2 = 0.81, P < 0.001). Although P and K concentrations were low in comparison to standard laboratory procedures, a calibration factor could address this issue. These results show that ISE technology can be implemented successfully for NO 3 –N, available K, and available P measurement with the Kelowna extractant.
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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 | 0.000 | 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 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".