Bibliographic record
Abstract
As the component sensors in swath sonar systems have improved, the focus on total system performance has turned increasingly to the remaining imperfections in the system integration. Of particular concern is. that faint but systematic across track ribbing often remains in otherwise high-quality data. Traditional field calibration procedures primarily look for the signature of static systematic error contributions. These procedures (the conventional patch test) only examine a subset of the pos sible systematic biases in the configu ration of an integrated swath sonar sys tem. Other systematic biases can cause dynamic rather than static signa tures in the resulting bathymetric data. These dynamic errors can be separat ed into those that produce errors that vary with periods in the ocean wave spectrum (most commonly referred to as the ‘wobbles’) and those whose period is dictated by the vessel's long period accelerations (turns and other course changes, obstacle avoidance and speed changes). Herein the theory behind the cause for a number of common wobble sources is examined. For the case of shallow water surveys, where the ping period is Figure 1: sun-illuminated terrain models of EM1002 bathymetric data in 30m of water. The top image shows data as originally collected with pronounced ship-track orthogonal ribbing. The bottom plot shows data after shifting the motion time series by-20ms. The peak to peak magnitude of the apparent rippling is on the order of +/-1.0-1.5 per cent (well within the required standard- IHO order 2). Data courtesy of the Geological Survey of Israel short with respect to the typical wave period, the wobble signatures can be easily discerned. The dif ferences in the signatures of each of the wobbles are highlighted allowing rapid classification and thus a means of removal of the underlying system atic bias.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".