Laboratory Calibration, In‐Field Validation and Use of a Soil Penetrometer Measuring Cone Resistance and Water Content
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Bibliographic record
Abstract
Concurrent and coincident measure of penetrometer cone resistance (PR) and water content (WC) were optimized by hourly in‐field validation of data from time domain transmissiometry (TDT) for WC and piezoelectric force sensor for PR. A piezoelectric force sensor coupled to a cone is followed by a helical wrapped TDT sensor on a single penetrometer shaft. Detailed laboratory calibrations, coupled with in‐field validations, were important to assure the quality of data, which facilitated detailed analyses of PR and WC patterns. The piezoelectric sensor relied on a calibrated spring for the in‐field validation. The calibration of the TDT sensor had three stages: a series of fluids of known dielectric constant; soil columns at known, variable water contents; and field soils at a range of ambient conditions. The penetrometer was used to study soil strength and WC behavior in time and space along 300‐m plots. The treatments were conventional and no‐till, each at two levels of traffic. The crop was corn ( Zea mays L.), continuous and in rotation with soybean [ Glycine max (L.) Merr.] and wheat ( Triticum aestivum L.). The PR vs. WC relationships for two depths (0.21 and 0.27 m), below the level of cultivation, were similar to those at the 0.10‐m depth for the nontrafficked no‐till plots. These relationships for the 0.21‐ and 0.27‐m depths were not influenced by tillage, traffic, and corn cropping system treatments. The variable depth of plowing in tilled plots was found to influence the data consistency for the 0.21‐m depth, indicating the penetrometer's high sensitivity to the soil conditions.
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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 | 0.000 | 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 it