Development of a vertical TDR probe to evaluate the vertical moisture profile in peat columns to assess biological clogging
Bibliographic record
Abstract
Time domain reflectometry (TDR) is used to monitor the moisture content in soils including peat. The objective of this study was to develop a TDR-based method to measure the vertical moisture profile in a peat biofilter operating in the field, where access to the filter is limited to the top surface. The moisture profile with depth can then be used to infer or assess the operating status of a filter in terms of clogging. The successful application of water content measurement using horizontal TDR probes has been demonstrated by many researchers. In this study, a single TDR probe was sequentially advanced into a peat column to estimate the vertical moisture profile and the results were compared to horizontal TDR and gravimetric measurements. The experiment was carried out in six peat columns during a drainage process and two columns during a clogging process. Water contents by the vertical and horizontal probes agreed very well, although the data was slightly more scattered in the columns subject to clogging due to signal attenuation. Total signal attenuation was observed with longer probes in clogged peat soils. However, there was a consistent discrepancy between the TDR measured water contents and those determined gravimetrically, which is believed to be caused by a systematic error, possibly error with the calibration curve.
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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 | 0.001 |
| 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".