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Record W4299451967 · doi:10.1051/kmae/2022009

Effects of Nile Tilapia (<i>Oreochromis niloticus</i>) on phytoplankton community structure and water quality: a short-term mesocosm study

2022· article· en· W4299451967 on OpenAlexaff
Liqiong Zhang, Xueying Mei, Yali Tang, Vladimir Razlutskij, Jiří Peterka, William D. Taylor, Luigi Naselli‐Flores, Zhengwen Liu, Chunfu Tong, Xiufeng Zhang

Bibliographic record

VenueKnowledge and Management of Aquatic Ecosystems · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsNile tilapiaOreochromisPhytoplanktonMesocosmTilapiaPeriphytonWater qualityNutrientBiomass (ecology)EutrophicationEnvironmental scienceTotal suspended solidsBiologyFisheryEcologyWastewaterEnvironmental engineeringFish <Actinopterygii>Chemical oxygen demand

Abstract

fetched live from OpenAlex

Nile tilapia is a highly invasive fish species, deliberately introduced into many lakes and reservoirs worldwide, sometimes resulting in significant ecosystem alterations. A short-term mesocosm experiment with and without Nile tilapia ( Oreochromis niloticus ) was designed to test the hypotheses that the presence of tilapia may affect phytoplankton community structure, increase nutrients availability in water column and deteriorate water quality. Nutrients, total suspended solids (TSS) and biomass of phytoplankton in different size classes (as Chl a ) were measured. We found that tilapia increased the total nitrogen (TN), total dissolved nitrogen (TDN), NH 4 + and TSS concentrations, deteriorating the water quality. In addition, under tilapia presence, the biomass of phytoplankton, as well as that of micro- and nano-phytoplankton, increased leading to a change in the structure of the phytoplankton assemblage. Moreover, a reduction in the biomass of periphyton was observed. Omnivorous tilapia is often dominant in tropical and subtropical waters, and removal of this fish may represent an effective management tool to improve the water quality.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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 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

Citations10
Published2022
Admission routes1
Has abstractyes

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