Correction of shallow-water electromagnetic data for noise induced by instrument motion
Bibliographic record
Abstract
Abstract An unexpected noise source has been found in mag-netic- and sometimes electric-field data recorded on the bottom of lakes in the Archean Slave craton (northwestern Canada) during warm seasons. The noise is the result of instrument motion and in some instances direct induction by wind-driven surface gravity waves when the lakes are not ice covered. The noise can be reduced or eliminated by prefiltering the data with an adaptive correlation noise-canceling filter using instrument tilt records prior to estimation of magnetotelluric (MT) response functions. Similar effects are to be expected in other shallow-water environments, and the adaptive correlation canceler is a suitable method to preprocess MT data to reduce motion-related noise in the magnetic field. This underscores the importance of ancillary tilt measurements in shallow-water MT surveys. In coastal or lake-bottom surveys, special efforts to reduce hydrodynamic effects on the instrument should also be pursued.
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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) | 0.000 | 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".