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Record W1977726462 · doi:10.5539/ijb.v2n2p158

Influences of Aquiculture on Ecological Environment

2010· article· en· W1977726462 on OpenAlexvenueno aff
Guangjun Wang, Xie Jun, Guangping Yin, Deguang Yu, Ermeng Yu, Haiying Wang, Wangbao Gong

Bibliographic record

VenueInternational Journal of Biology · 2010
Typearticle
Languageen
FieldEngineering
TopicIndustrial Technology and Control Systems
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsChinaPollutionAquatic ecosystemWater qualityQuality (philosophy)Environmental scienceAquatic environmentEnvironmental qualityEcologyBusinessNatural resource economicsEnvironmental protectionEnvironmental planningEnvironmental resource managementEnvironmental engineeringGeographyBiologyEconomics

Abstract

fetched live from OpenAlex

Since reforming and opening to the outside world, Chinese aquiculture has developed very quickly. The totaloutput of aquiculture has been ranking first in the world over ten years, but it still has many problems. In thearticle, these problems which existing in the aquiculture of China are listed, and they have seriously influencedthe quality of aquatic products of China and destroyed the whole ecological environment of aquiculture. Thefactors influencing the water environment include: (1) residual feeds and excrements, (2) chemical medicinesused in aquiculture, (3) escaping aquatic animals. And the aspects which were influenced including: (1) theinfluence on the physiochemical factor of water, (2) the influence on the bottom matters, (3) the influence on theplankton, and (4) the influence on the bottom dwellers are analyzed. To reduce the influence and pollution ofaquiculture on the ecological environment, enhancing the quality of composite feed is one important approach.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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 designObservational
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

Citations6
Published2010
Admission routes1
Has abstractyes

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