Assessing water position through distributed temperature sensing using Rayleigh-based optical frequency-domain reflectometry: a laboratory feasibility study
Bibliographic record
Abstract
This study demonstrates the ability of the Rayleigh-based phase-noise compensated optical frequency-domain reflectometry (PNC-OFDR) sensing method to monitor the distributed temperature field with an ultra-short data acquisition period of 2 ms, a spatial resolution of 2 cm, and a temperature resolution of 0.1 °C. A heating cable (H-cable) was embedded within a cylindrical concrete mortar specimen and subjected to various heating powers. A sensing optical cable (temperature measurement cable) was placed adjacent to the H-cable to monitor the temperature distribution continuously. Two water-holding boxes were installed along the specimen at two positions to retain water. The study’s results indicated that the PNC-OFDR technique demonstrated a high sensitivity to even small temperature changes, enabling it to pinpoint water locations at two distinct points accurately. The research determined the minimal heating power required to successfully locate the water positions. The magnitude of the heating power exerted a significant impact on the temperature change. Three distinct phases of temperature increment were observed for a given heating period: rapid, fast, and gentle increase. The insights gained from this study have the potential to be applied in natural fields, allowing for the detection of groundwater and seepage phenomena in vulnerable slopes.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".