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Record W373158566

汽水域調査のためのローコスト・コンパクトな音響調査機器(サイドスキャンソーナー)のシステム化

2004· article· ja· W373158566 on OpenAlexaboutno aff
清和 西村, 正人 上嶋, 隆夫 徳岡

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2004
Typearticle
Languageja
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.013
GPT teacher head0.229
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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