Effect of Biochar on TDR-Based Volumetric Soil Moisture Measurements in a Loamy Sand Podzolic Soil
Bibliographic record
Abstract
Considering the increased interests in biochar (BC) as a soil amendment and a growing media substrate in agriculture, we evaluated the effect of BC incorporation on TDR (time-domain reflectometer)-based volumetric soil moisture content (VSMC) estimations in a loamy sand podzolic soil. Two commercial BC types (powdered—BCP, and granular—BCG) were mixed in different rates (w/w) with a podzolic soil. The dielectric constants measured using a TDR cable tester (MOHR CT 100) were converted to VSMC. Three commonly used models: (i) Topp’s equation, M-1; (ii) mixing model, M-2; and (iii) the forest soil model, M-3, were used. The accuracy of the estimated VSMC using these three models was statistically compared with measured VSMC. BCP at lower rates produced very similar results to the actual VSMC with M-1 and M-2 but deviated with increasing rates. The M-3 showed a non-linear relationship with measured VSMC. In BCG treatments, all models overestimated the VSMC. BCG rates higher than 15% (w/w) resulted in highly attenuated TDR waveforms and the signal was completely dissipated when rates higher than 50% (w/w) were used (typical application for field soils is less than 5% w/w). These results showed that predictions of the soil moisture content based on the soil dielectric constant might not be feasible for tested podzolic soils amended at high BC rates.
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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 | 0.000 | 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 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".