Performance of a Capacitance‐Type Soil Water Probe in a Well‐Drained Sandy Soil
Bibliographic record
Abstract
Most soils in the Central Florida Ridge (CFR) area are Entisols that contain >95% sand, <3% clay, and <2% organic matter. Field capacity (θ fc ) is commonly ∼0.08 m 3 m −3 Therefore, accurate estimation of soil water content (θ v ) is important in these soils. The objective of this study was to evaluate the performance of ECH 2 O probes when estimating θ v for scheduling irrigation in CFR soils. Probes were tested for (i) probe‐to‐probe output variability, (ii) soil volume sampled, (iii) sensitivity to salinity, temperature, and air pockets close to the sensor surface, (iv) pockets of very dry soil close to the sensor surface, and (v) performance after installation in the field. According to the calibration, a 1% change in water content corresponds to a probe output of 17 mV. Laboratory testing suggested that output variability from probe to probe can be a problem in these soils. The sampling volume of the probe was within 1.5 cm from either side of the sensor surface. Salinity induced during fertigation increased the output by about 200 mV, and for each 1°C drop in temperature, the sensor output dropped by 2.3 mV. When the bulk density was changed from 1.56 to 0.94 Mg m −3 , the output decreased by 3.5 MV for each 1% drop in air‐filled porosity. When very dry soil lenses with <0.01 m 3 m −3 θ v were associated with the probe surface, the probe failed to sense the wet soil even 1 cm away from the sensor surface. Sensor failure was common due to water leaking into the circuit when sealing material deteriorated or casing material was damaged by insects. These issues need to be addressed before the probes can be considered reliable to estimate θ v or used in automated irrigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".