Multibeam bathymetry optimization problem based on geometric modeling and simulated annealing
Bibliographic record
Abstract
Echo sounding is a technique commonly used in marine bathymetry to measure the depth and topography of water bodies. This paper combs the development process of echo sounding technology, briefly describes the principle of multibeam bathymetry, and its application in ocean bathymetry and water conservancy engineering. In order to get the optimized scheme of the survey line in the rectangular sea area, this paper firstly draws a spatial 3D scatter plot using the attached data to observe the general shape of the seabed surface. Then, polynomial fitting is utilized to fit the surface to all points to obtain the surface equation. From the scatter plot, it can be seen that the seafloor slope is relatively gentle, and if the formula for the coverage width when the seafloor slope is horizontal can be used for calculation, the model will be greatly simplified. The programming in this paper verifies the reasonableness of the conjecture, so the simplified formula can be used for subsequent calculations. In order to determine the number of survey lines, a simulated annealing algorithm was used, and finally, we designed 31 parallel survey lines in the north-south direction, with a total length of 155 nautical miles, and the omitted sea area accounted for 1.71% of the total area to be surveyed, and in the overlapping area, the overlap rate of the part of the overlap rate of more than 20% had a total length of zero.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
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 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".