Bibliographic record
Abstract
The Cramer-Rao bound is employed to establish a lower bound on the estimation accuracy of bottom detection and angle estimation methods for swath bathymetry. The angle estimation bound is converted to a distance along an arc at the given range and then compared to the range estimation accuracy obtained for bottom detection methods. It is determined that for bottom detection methods the accuracy decreases as the angle for which the estimation is made moves from vertical to horizontal. Also, if the backscatter decorrelates across the beam (the usual situation in practice), the estimation accuracy plateaus and increasing the signal-to-noise ratio no longer increases the accuracy. Angle estimation methods tend to be more accurate away from the nadir angle (vertical for a flat bottom), complementing bottom detection methods, and the accuracy does not plateau as quickly with increasing signal-to-noise ratio. Therefore, with regard to performance with increasing signal-to-noise ratio, angle estimation has an advantage over bottom detection. It is also shown that the accuracy of angle estimation techniques is affected detrimentally by acoustic pulse spread on the bottom and in a relative sense the effect is more severe for large arrays. Therefore, there is a potential for obtaining good swath bathymetry with small arrays using angle estimation techniques.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".