On the time decay constant of AEM systems: a semi-heuristic algorithm to validate calculations.
Bibliographic record
Abstract
The time decay constant or “tau” of airborne electromagnetic (AEM) systems is commonly used to indicate the presence and relative conductivity or conductance of conductors in the survey area. In fact, it is not a constant because it depends on the system, the survey design and the method of calculation. The system dependence is a consequence of parameters relating to the acquisition and pre- and post-processing of the signal. Here, we propose a method for calculating tau, which is the time at which the transient voltage decays to 37%, or V37, of some initial value. The model utilises a semi-heuristic algorithm to estimate V37 for each transient in the database and then calculates the delay time at which that voltage is measured, which estimates tau. No calculation is involved with the data, instead, tau is given by a weighted average of the delay times associated with windows either side of the V37 value. We illustrate how this algorithm works using data collected using MEGATEM II at the Reid Mahaffy test site. The results show good agreement between tau-grids reported previously and those calculated using our V37 method. To account for all effects due to the acquisition and processing of EM data, the algorithm allows emphasis to be shifted away from early-time to late-time parts of the transient. It is envisaged that because this method does not apply any mathematical operation to the data it may serve as a robust means of validating other methods.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".