Bibliographic record
Abstract
When geological surveys or environmental studies are carried out in brackish water lakes like Lake Nakaumi and Lake Shinji, a sub-bottom profiler (SBP), which images the geological structure of the lake sediment, and sidescan sonar (SSS), which reveals the microtopography of the lake bottom, are essential survey equipment. However, conventional survey equipment was designed for marine operations, and as a result it is large and difficult to use in lakes. A low-cost and compact sidescan sonar and sub-bottom profiler system was developed which can be deployed from a small boat, and easily used in the survey of brackish water lakes. The trade name of the digital sidescan sonar is SportScan (Imagenex Technology Corp., Canada) and the trade name of the sub-bottom profiler is StrataBox (SyQwest Inc., USA). In this paper, we focus on the sidescan sonar. The low-cost sidescan sonar is composed of a towfish connected directly to a power supply (10-16 VDC) and notebook PC through the shipboard towing cable. The operating frequency of the sidescan sonar is 330 kHz, the available operating range is 15m-120m, and the best towing speed is 2-3 knots. A survey of Lake Nakaumi was carried out by combining sidescan sonar/sub-bottom profiler and a DGPS receiver with navigation based on the map software KASHMIR 3 D. This sidescan sonar survey produced a mosaic map of the sediment surface of Lake Nakaumi.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".