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Record W2123318913 · doi:10.5539/mas.v4n9p14

Study on the Method of the Laser Backward Detection of Underwater Bubble Films Based on the Spatial Analysis

2010· article· en· W2123318913 on OpenAlexvenueno aff
Jianwei Zhang, Yang Kun-tao, Zhiguo Ma

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBubbleUnderwaterScatteringLight scatteringOpticsMie scatteringLaserMaterials sciencePhysicsTransmission (telecommunications)Computer scienceMechanicsTelecommunicationsGeology

Abstract

fetched live from OpenAlex

The backward light scattering characteristics of the water body and the bubble films are analyzed by the light transmission theory under water and the Mie light scattering theory, and the result shows that their spatial distributions are different. The backward light scattering of the water body is the monotone decreasing smooth curve, but the backward light scattering of bubble films presents complex change. Based on that, a new underwater bubble films laser backward detection method is proposed in this article. By the multiunit detector, this method could receive the backward scattering light signals, and detect the bubble films by the spatial analysis. The experiment result indicates that this method could realize the effective detection of the underwater bubble films, especial the detection of the distant bubble films.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.222
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2010
Admission routes1
Has abstractyes

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