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Record W4213306798 · doi:10.1080/17445302.2022.2032990

Effect of bottom counterweight and cable distribution on the hydrodynamic response of the gravity net cage

2022· article· en· W4213306798 on OpenAlexaff
Xiangqian Zhu, Qingxian Bi, Xinyu Li, Ryan S. Nicoll, Gangqiang Li, Hui Ren

Bibliographic record

VenueShips and Offshore Structures · 2022
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDynamic Systems Analysis (Canada)
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsCounterweightCollarTension (geology)CageStructural engineeringEngineeringMechanicsUnderwaterMarine engineeringGeologyPhysicsCompression (physics)

Abstract

fetched live from OpenAlex

To investigate the influence of the bottom counterweight and cable distribution on hydrodynamic response of the gravity net cage, a single cylindrical gravity net cage with different cable distributions and counterweights was analysed by numerical simulation. The floating collar was simplified as a hollow ring that has the same mechanical properties as one double-row floating pipe. The influence of the current, wave and cable distribution on the floating collar motions and cable loads were investigated. Analysis results illustrated that the volume reduction coefficient of net structure and the maximum tension of cable are mainly affected by the counterweight and cable distribution, respectively. In addition, the deformation of the floating collar is mainly affected by the wave height. The cylindrical gravity net cage with the 80 kg × 32 bottom counterweight and the ‘*’-layout cable distribution is fit for the sea state of the Yellow Sea Cold Water Mass.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.195
Teacher spread0.191 · 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
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

Citations4
Published2022
Admission routes1
Has abstractyes

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