EVALUATION OF NITRATE AND POTASSIUM ION-SELECTIVE MEMBRANES FOR SOIL MACRONUTRIENT SENSING
Bibliographic record
Abstract
On-the-go, real-time soil nutrient analysis would be useful in site-specific management of soil fertility. The rapidresponse and low sample volume associated with ion-selective field-effect transistors (ISFETs) make them good soil fertilitysensor candidates. Ion-selective microelectrode technology requires an ion-selective membrane that responds selectively toone analyte in the presence of other ions in a solution. This article describes: (1) the evaluation of nitrate and potassiumion-selective membranes, and (2) the investigation of the interaction between the ion-selective membranes and soilextractants to identify membranes and extracting solutions that are compatible for use with a real-time ISFET sensor tomeasure nitrate and potassium ions in soil. The responses of the nitrate membranes with tetradodecylammonium nitrate(TDDA) or methlytridodecylammonium chloride (MTDA) and potassium membranes with valinomycin were affected by bothmembrane type and soil extractant. A TDDA-based nitrate membrane would be capable of detecting low concentrations in soils to about 10-5 mole/L NO3 -. The valinomycin-based potassium membranes showed satisfactory selectivity performancein measuring potassium in the presence of interfering cations such as Na+, Mg2+, Ca2+, Al3+, and Li+ as well as provideda consistent sensitivity when DI water, Kelowna, or Bray P1 solutions were used as base solutions. The TDDA-based nitratemembrane and the valinomycin-based potassium membrane, used in conjunction with Kelowna extractant, would allowdetermination of nitrate and potassium levels, respectively, for site-specific control of fertilizer application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".