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Record W4403963756 · doi:10.5376/ija.2024.14.0023

Emerging Contaminants in Aquatic Ecosystems: Sources, Effects, and Mitigation Approaches

2024· article· en· W4403963756 on OpenAlexvenueno aff
Xueli Zhang, Xiaohong Liu

Bibliographic record

VenueInternational Journal of Aquaculture · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemEnvironmental scienceAquatic ecosystemContaminationHeavy metalsAquatic environmentEnvironmental resource managementEcologyEnvironmental protectionEnvironmental chemistryBusinessEnvironmental planningBiologyChemistry

Abstract

fetched live from OpenAlex

This study explores emerging pollutants in aquatic ecosystems, their sources, impacts, and mitigation methods. With the progress of industrialization and population growth, more and more emerging pollutants (such as drug residues, pesticides, heavy metals, etc.) enter water bodies through various pathways, which have a profound impact on aquatic species and ecosystem services. The problem with the study is that the accumulation and continuous exposure of these pollutants not only pose a toxicological threat to aquatic organisms, but may also affect human health through the food chain and water sources. Therefore, it is important to identify the sources and pathways of emerging pollutants and their impacts on ecology and health, develop effective monitoring and treatment technologies, and promote adaptive policies on a global scale, providing a basis for further governance and protection work.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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