Evaluation of a multi‐sensor for measuring solution electrical conductivity in coir
Bibliographic record
Abstract
Abstract The simultaneous measurements of both volumetric water content (θ) and electrical conductivity of pore solutions (ECw) are essential for nutrient solution management within soilless culture systems. The present study was carried out in order to evaluate a capacitance sensor (METER 5TE) for the measurement θ and ECw within coir. Following the evaluation of the spatial sensitivity of the 5TE sensor, a calibration experiment was executed with six levels of θ (0.30, 0.35, 0.40, 0.45, 0.50, and 0.55 m3 m−3) and four levels of KCl solutions (1.0, 3.0, 5.0, and 7.0 dS m−1). The measured relationship between θ and dielectric permittivity (εa) for coir significantly deviated from Topp's equation for organic soil, but was similar to the calibration equation for growing media from the sensor manufacturer and for peat soils. The Hilhorst (2000) model recommended by the manufacturer overestimated ECw within coir. On the other hand, accounting for EC dependency on the real part of the complex dielectric permittivity of the solution within the Hilhorst model will greatly improve ECw prediction. In contrast, the Rhoades (1976) model was more capable of describing the dependence of the apparent soil electrical conductivity (ECa) on θ and ECw for the coir samples for ECa < 2.9 dS m−1. Hence, when ECw−ECa−θ relations are known, the capacitance sensor is a powerful tool for horticultural practices because a single measurement can yield both θ and ECw.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".