Determining the clog state of constructed wetlands using an embeddable Earth’s Field Nuclear Magnetic Resonance probe
Bibliographic record
Abstract
The recent rise in interest of green technologies has led to significant adoption of the constructed wetland as a waste water treatment technique. This increased popularity has only been mired by the decline in operational lifetime of wetland units, leading to the need for more regular, time consuming, and expensive rejuvenation techniques to be performed than initially anticipated. To extend operational lifetimes and increase efficiency of wetland units, it is crucial to have an accurate method to determine the internal state of the wetland system. The most important parameter to measure within the reed bed is the clog state of the system, which is representative of the overall system health. In previous work, magnetic resonance (MR) measurements, parameters of T1 and T2eff, have been demonstrated as extremely powerful tools to determine the internal clog state of a wetland [1, 2]. Measurements have been performed in a laboratory setting, using low field permanent magnet arrangements. This work presents an Earth’s Field Nuclear Magnetic Resonance (EFNMR) probe suitable for in situ measurements within constructed wetlands. We show T2eff and T1 measurements using the EFNMR probe. T1 values are shown to be sensitive to the change in the clog state with 1498 ms for the thickly clogged sample and 2728 ms for the thinly clogged sample. T2eff values are shown to be marginally more sensitive to clog state with 630 ms for a thickly clogged sample and 1212 ms for the thinly clogged sample. This gives distinguishable variation within both parameters suggesting that this probe is suitable for embedding into an operational constructed wetland. This work was conducted as part of an EU FP7 project to construct an Automated Reed Bed Installation, “ARBI”.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".