The effect of low pass filters in the Molikpaq data acquisition system on "phase lock" interaction signals
Bibliographic record
Abstract
During the deployment of the Molikpaq in the Canadian Beaufort Sea, in the 1980’s, various data signals were recorded by a digital data acquisition system. As part of this system an analog lowpass anti-aliasing filter was incorporated. The frequency response of this filter has been found to be far from ideal, resulting in significant changes in the amplitudes and wave shapes of data sampled at 50 Hz. Because of this distortion the severity and understanding of ‘phase lock’ interactions may have been in error. As an example, peak caisson accelerations have been reported in the literature to be up to about 10%g. The current investigation into the effect of the filter indicates that the peak accelerations are 50% to 75% larger. In this paper, the authors discuss the filter frequency response and its effect on the strain-gauge and acceleration data recorded in the 50 Hz (“Burst”) files with the emphasis on phase lock interactions. The authors also discuss aspects of the interpretation of the phase lock phenomena. It will be shown that there are consequences for the material contained in Section A.8.2.6, dealing with Dynamic Ice Actions, of the proposed International Organization for Standardization (ISO) 19906 Standard.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".