A heuristic method of removing micro-pulsations from airborne magnetic data
Bibliographic record
Abstract
An automatic, heuristic method has been developed for the removal of micro-pulsations from airborne magnetic data to improve the geological integrity of data.This method was applied to data collected in a high-sensitivity survey flown off the coast of Nova Scotia, Canada from September to October 2000.This method permitted the diurnal data measured on land to be used to subtract micropulsation variations from the airborne data.Small differences in the variations are allowed between the data acquired at the base station and on the airborne platform.This is because conductivity contrasts between the land and sea water will change the amplitude and phase of the micropulsation.The new method adjusted the land based diurnal data to match the phase and amplitude of the micropulsation measured in the airborne data.This adjusted micropulsation was then removed from the data.This improved the final leveled data so that obvious micropulsations are not evident in the data.This noise reduction is critical, as any filtering which may be applied, cannot remove diurnal events from the airborne data without possibly removing geologic signal.The procedure will produce higher quality data that better show weaker features and patterns than the standard processing produces, such as subtraction of the base station data.
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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 | 0.001 |
| 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 | 0.001 | 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 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".