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Record W3027281685 · doi:10.3390/w12051444

Effects of Crucian Carp (Carassius auratus) on Water Quality in Aquatic Ecosystems: An Experimental Mesocosm Study

2020· article· en· W3027281685 on OpenAlexaff
Yehui Huang, Xueying Mei, Lars G. Rudstam, William D. Taylor, Jotaro Urabe, Erik Jeppesen, Zhengwen Liu, Xiufeng Zhang

Bibliographic record

VenueWater · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsCrucian carpMesocosmEutrophicationPeriphytonWater qualityPhytoplanktonTurbidityEnvironmental scienceBiomass (ecology)Water columnOmnivoreFisheryBiologyNutrientEcologyFish <Actinopterygii>Predation

Abstract

fetched live from OpenAlex

The presence of omnivorous fish is known to affect aquatic ecosystems, including water quality. The effect, however, depends on the species in question, and our knowledge is limited on the effect of omnivorous crucian carp (Carassius auratus), a common and often the most numerous fish species in eutrophic shallow lakes in China. We conducted a 70-day outdoor experiment in mesocosms with and without crucian carp to examine whether this species adversely affects water quality by increasing the levels of total nitrogen (TN) and total phosphorus (TP), thereby stimulating the biomass of phytoplankton and increasing water turbidity. Compared with carp-free controls, the presence of crucian carp resulted in higher TN and TP in the water column, greater phytoplankton biomass and lower periphyton biomass, measured as chlorophyll a. Total suspended solids (TSS) also increased in the presence of fish. We conclude that crucian carp can increase TN and TP, enhance phytoplankton biomass, and increase water turbidity, thereby contributing significantly to the deterioration of the water quality. In addition to controlling external nutrient loading, the removal of crucian carp may help to improve water quality in warm shallow eutrophic lakes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.899

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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