Field calibration of an impedance soil water probe for the shallow seedbed across field topography
Bibliographic record
Abstract
Abstract Impedance soil water probes enable frequent and non‐destructive determination of soil water status in situations where gravimetric soil sampling is too demanding of time and sampling space. The ThetaProbe is an impedance soil water probe requiring calibration for local soil conditions, because measurement accuracy can be affected by properties of the soil. Often, only a single calibration is performed for an experimental site. An experiment investigating the seedbed to 75‐mm depth across a field topography with variable soil properties was examined to determine which soil properties affected the calibration of the ThetaProbe, and if soil‐specific calibration was required to derive suitable estimates of the water status in the experiment. Experimental factors examined included hillslope aspect, hillslope position, crop residue and soil depth. Soil properties, other than volumetric water content, significantly affecting the probe measurements were bulk density, electrical conductivity and temperature. The probe underestimated soil water at very low water contents, and overestimated soil water at contents greater than 11 m 3 m −3 , compared with gravimetric measurements. A single calibration, not corrected for hillslope position at a water content of 20 m 3 m −3 , overestimated water content by 0.02 m 3 m −3 in the summit hillslope position and underestimated water content by 0.04 m 3 m −3 in the toeslope position. A single calibration, not corrected for soil depth at a water content of 20 m 3 m −3 , overestimated water content by 0.02 m 3 m −3 in the 0‐ to 25‐mm soil layer and underestimated water content by 0.03 m 3 m −3 in the 50‐ to 75‐mm layer. The complexity of microsites in a shallow seedbed requires soil‐specific calibration in field experiments containing heterogeneous soil properties.
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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) | 0.000 | 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) | 0.001 | 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".