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Record W4410247309 · doi:10.5376/ijms.2024.14.0040

Toxicological Studies of Fish and Fish Cells in Vitro and in Vivo

2024· article· en· W4410247309 on OpenAlexvenueno aff
Guilin Wang, Chen Liang

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsFish <Actinopterygii>In vivoIn vitroBiologyChemistryFisheryBiotechnologyBiochemistry

Abstract

fetched live from OpenAlex

With the rapid development of industrialization, urbanization, and agriculture, various pollutants pose significant threats to fish health and may harm human health through the food chain. This study comprehensively explores advancements in in vitro and in vivo toxicological studies of fish and fish cells, revealing the mechanisms by which various pollutants impact the immune system, nervous system, and overall physiological functions of fish. In vitro models, such as the rainbow trout cell lines (RTgill-W1 and RTgutGC), demonstrate high efficiency in predicting toxicity, while whole fish experiments provide a realistic ecological context for assessing the comprehensive effects of pollutants. The review focuses on the synergistic toxicity of emerging contaminants and their potential for bioaccumulation, emphasizing future directions based on omics technologies and high-throughput methods to optimize toxicological research approaches and enhance pollution monitoring capabilities.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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