Soil Water Measurement Using Actively Heated Fiber Optics at Field Scale
Bibliographic record
Abstract
Several studies have demonstrated the potential of actively heated fiber optics (AHFO) to measure soil water content (SWC) at high spatial and temporal resolutions. This study tested the feasibility of the AHFO technique to measure soil water in the surface soil of a crop grown field over a growing season using an in-situ calibration approach. Heat pulses of five minutes duration were applied at a rate of 7.28 W m−1 along eighteen fiber optic cable transects installed at three depths (0.05, 0.10 and 0.20 m) at six-hour intervals. Cumulative temperature increase (Tcum) during heat pulses was calculated at locations along the cable. While predicting commercial sensor measurements, the AHFO showed root mean square errors (RMSE) of 2.8, 3.7 and 3.7% for 0.05, 0.10 and 0.20 m depths, respectively. Further, the coefficients of determination (R2) for depth specific relationships were 0.87 (0.05 m depth), 0.46 (0.10 m depth), 0.86 (0.20 m depth) and 0.66 (all depths combined). This study showed a great potential of the AHFO technique to measure soil water at high spatial resolutions (<1 m) and to monitor soil water dynamics of surface soil in a crop grown field over a cropping season with a reasonable compromise between accuracy and practicality.
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 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 | 0.000 | 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) | 0.001 | 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".