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Record W4379054805 · doi:10.1111/jwas.12990

The environmental impact and development direction of grass carp, <i>Ctenopharyngodon idella,</i> aquaculture

2023· article· en· W4379054805 on OpenAlexaff
Shen Hong‐ye, Zhaojun Gu, Cheng Guofeng, Zhu Hao

Bibliographic record

VenueJournal of the World Aquaculture Society · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsGrass carpAquacultureFisheryEutrophicationBiologyLife-cycle assessmentFish farmingPolycultureEnvironmental pollutionBighead carpEnvironmental scienceAnimal scienceCarpEnvironmental protectionProduction (economics)EcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Grass carp, Ctenopharyngodon idella , is the largest freshwater aquaculture fish species worldwide. However, its environmental impacts are increasingly controversial. In this paper, we considered the production of a 1500 g commercial grass carp as an example, analyzed through a life cycle assessment. The results showed that the indicators of global warming potential (GWP), acidification potential, eutrophication potential, freshwater eco‐toxicity potential (FAETP), land competition (LC), and fossil energy consumption of producing 1 kg of grass carp were equivalent to 5.7267 kg of CO 2 , 0.0648 kg of 1,4‐DCB0, 0.0010 kg of P, 0.0276 kg of SO 2 , 8.2951 m 2 , 0.3491 kg of oil, respectively, and were mainly from feed processing and water pollution. Compared with pig, beef, and sheep production, grass carp production has lower environmental impacts, but in terms of GWP, FAETP, and LC were significantly higher than chicken production, especially water pollution and discharge, which is an important consideration. This study clarifies the direction of grass carp production and key focus areas include producing low carbon and nitrogen emission feed, application of ecological engineering aquaculture system, intelligent mechanization technology and equipment.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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 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

Citations20
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the World Aquaculture SocietySame topicAquaculture Nutrition and GrowthFrench-language works237,207