The Use of Wavelet Analysis to Derive Infiltration Rates from Time‐Lapse One‐Dimensional Resistivity Records
Bibliographic record
Abstract
As part of a study to understand factors impacting the efficiency of an artificial recharge pond in Watsonville, CA, a time series of resistivity measurements was made using a permanently installed one‐dimensional resistivity probe. Measurements were made in the top 2 m of sediment with data acquired every 30 min. There was an observed diurnal signal in these data due to daily temperature fluctuations in the pond water. By viewing this signal as a thermal tracer, we used the movement of the associated thermal front to estimate infiltration rates from the resistivity data. We developed a wavelet‐based method for calculating lag times of the thermal front between measurement locations. As part of this algorithm, we tested the statistical significance of a given signal and automatically rejected calculated lag times that were associated with signals below a given confidence interval. We included a linear inversion routine for calculating the velocity of the thermal front from the calculated lag times. Using the thermal velocity, we estimated an infiltration rate at the resistivity probe that decreased from approximately 3.5 to 1 m d −1 during a period of 18 d. Resistivity data have a distinct advantage over direct temperature measurements: a resistivity measurement is sensitive to changes outside the region disturbed by instrument emplacement. While our processing approach was demonstrated on the presented resistivity data, it is equally valid for use with direct temperature measurements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".