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Record W4385720996 · doi:10.5376/ija.2023.13.0005

Design and Construction of Modern Marine Ranching: Technologies, Methods, and Challenges

2023· article· en· W4385720996 on OpenAlexvenueno aff
Rudi Mai

Bibliographic record

VenueInternational Journal of Aquaculture · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsMarine conservationContext (archaeology)Emerging technologiesEnvironmental planningBusinessEnvironmental resource managementRisk analysis (engineering)Computer scienceEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

With the growing global population and increasing demand for marine products, the design and development of marine ranching have become increasingly important. This paper provides an in-depth exploration of the design concepts, construction technologies, and methods of modern marine ranching, and comprehensively analyzes the major challenges faced and corresponding strategies. Addressing different marine environments, various design principles are discussed, such as diversified aquaculture, optimal utilization of spatial resources, and environmental protection considerations. Furthermore, cutting-edge construction technologies and methods are introduced, including China's "Guoxin-1," which utilizes high-tech and innovative equipment to enhance farming efficiency and reduce environmental impacts. However, the construction and development of marine ranching face numerous challenges, such as climate change, marine pollution, and technological bottlenecks. In this context, potential solutions are proposed, including adopting new technologies, improving management systems, and implementing policy adjustments. Lastly, this paper provides detailed descriptions and in-depth analyses of five representative modern marine ranching cases worldwide, aiming to provide valuable references for the design and construction of future marine ranching.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.283
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations2
Published2023
Admission routes1
Has abstractyes

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