Predictive filter calculation of primary fields in a fixed-wing time-domain AEM system
Bibliographic record
Abstract
Abstract High-altitude data are used to calibrate a least-squares recursion filter that estimates the continually changing primary field of an airborne electromagnetic (AEM) system. The coupling changes in fixed-wing towed-bird systems generate “geometry noise” that in the on-time can be much larger than the ground secondary response. The LSQ filter accurately predicts the high-altitude primary field of a fixed-wing system. The filter is then applied to survey-altitude data to estimate the primary field for subsequent subtraction. After removing the primary field, a spatially consistent difference is detected over a range of delay times, as would be expected from geologic responses. A map of decay constants is produced for the survey area using the data corrected by the predicted primary field. Comparing these time constants with those computed from the conventional method, the maximum decay constant detectable was seven times larger. Thus, the new process can characterize conductors that are seven times more conductive than the conventional processing method. The residual primary field occurs at relatively high frequencies compared to targets of interest. At low frequencies (<0.2Hz), we estimate that 28% of the survey-altitude primary field remains whereas only 1% of calibration flight primary is not predicted.
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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 | 0.002 |
| 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) | 0.002 | 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".