Real-time aggregation forces monitoring in varied soil particle sizes using fiber bragg grating sensors
Bibliographic record
Abstract
The study investigates the effectiveness of Fiber Bragg Grating (FBG) sensors in measuring real-time aggregation forces across soils with varying particle sizes. Traditional measurement techniques are invasive, static, and lack real-time capability, making FBG sensors a promising technology for real-time monitoring. A laboratory experiments was conducted using FBG sensors embedded within soil samples of different particle sizes (0.125 mm, 0.425 mm, 0.85 mm, 1.18 mm, and 2 mm) with aggregation forces measured under controlled conditions simulating various water content levels. Data were collected continuously over a specified period to assess the dynamic response of the soils. The results showed a strong correlation between soil particle size and aggregation force, with larger particles exhibiting higher compaction-derived aggregation forces due to enhanced mechanical interlocking and reduced void space under load, while finer particles showing greater cohesion from higher surface area-to-volume ratios. This distinction arises because coarse particles (2.0 mm) transmit forces primarily through gravitational settling and frictional resistance, whereas fine particles (0.125 mm) rely on cohesive surface interactions. Wavelength shifts recorded by FBG sensors confirmed their reliability in detecting force changes, with finer soils yielding more pronounced sensor responses. These findings have significant implications for soil health assessment, agricultural management, and environmental engineering, particularly in optimizing tillage and soil stabilization strategies. The study demonstrates the potential of FBG sensors for real-time soil monitoring and recommends further exploration of sensor integration with field-scale applications and varying environmental conditions to enhance soil management practices.
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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 | 0.000 |
| 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".