Actively heated fiber optics technique to quantify spatio-temporal dynamics of soil water from point to field scale
Bibliographic record
Abstract
A lack of high resolution soil water data has limited the ability to examine and quantify the spatiotemporal dynamics of soil water at field scale.The main objective of this research was to develop a sensing technique to measure soil water at high spatial and temporal resolutions using actively heated fiber optics (AHFO) and to quantify the spatiotemporal dynamics of soil water at point and field scales.The first study developed calibration relationships using various heating strategies of three contrasting textured soils.Results showed that the sensitivity of the thermal response, normalized cumulative temperature increase (NTcum) increased and the predictive error (root mean square error, RMSE) of soil water decreased (from 7 to 2 %) with an increase in power intensity and heating duration.Nevertheless, moderate power intensities (e.g. 10 and 5 Wm -1 ) clearly showed an increase in the sensitivity of NTcum and a decrease in predictive error (2-3 %) with the extended heating duration.The second study examined the feasibility of the sensing technique to monitor spatiotemporal soil water dynamics and to provide an accurate estimate of average soil water content at laboratory soil column scale using a drip irrigation experiment.Results suggested that the sensing technique could accurately (RMSE 2-4 %) monitor the threedimensional (3D) wetting patterns through time under drip irrigation and was able to provide a more accurate estimate of the average soil water content of the column compared to that of pointbased sensors.The next study examined the feasibility of the sensing technique to measure soil water at field scale using an in-situ calibration approach and the results showed the ability to measure soil water at high spatial (0.5 m) and temporal (6 h) resolutions along eighteen fiber optic cable transects at three depths (0.05, 0.10 and 0.20 m) with a predictive error of 3-4 %.The fourth study examined the scale and location dependent time stability of spatial patterns of soil water storage (SWS) using the high-resolution spatiotemporal measurement of soil water content.The results showed strong intra-seasonal time stability across larger scales (> 10 m) and most of the DEDICATION This thesis is dedicated to my parents for their love, endless support, encouragement and sacrifices.
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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 | 0.000 | 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 | 0.001 |
| 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".