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Record W4387997330 · doi:10.1016/j.ecoenv.2023.115609

Hypoxia-induced physiological responses in fish: From organism to tissue to molecular levels

2023· review· en· W4387997330 on OpenAlexaff
Zhengxi Wang, Decheng Pu, Jishu Zheng, Peiyuan Li, Hongjian Lü, Xiuli Wei, Mai Suan Li, Dong‐Sheng Li, Lihong Gao

Bibliographic record

VenueEcotoxicology and Environmental Safety · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsHypoxia (environmental)EutrophicationBiologyOrganismFish physiologyAquacultureOxidative stressFish <Actinopterygii>Fish killEcologyCell biologyZoologyPhysiologyOxygenFisheryBiochemistryGeneticsChemistryAlgal bloom

Abstract

fetched live from OpenAlex

Dissolved oxygen (DO) in water bodies is a prerequisite for fish survival and plays a crucial role in fish growth, development, and physiological processes. However, with increasing eutrophication, greenhouse effects, and extreme weather conditions, DO levels in aquatic environments often become lower than normal. This leads to stress in fish, causing them to exhibit escape behavior, inhibits their growth and development, and causes tissue damage. Moreover, oxidative stress, decreased immune function, and altered metabolism have been observed. Severe hypoxia can cause massive fish mortality, resulting in significant economic losses to the aquaculture industry. In response to hypoxia, fish exhibit a series of behavioral and physiological changes that are self-protective mechanisms formed through long-term evolution. This review summarizes the effects of hypoxic stress on fish, including the asphyxiation point, behavior, growth and reproduction, tissue structure, physiological and biochemical processes, and regulation of gene expression. Furthermore, future research directions are discussed to provide new insights and references.

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)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.306
Teacher spread0.239 · 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

Citations100
Published2023
Admission routes1
Has abstractyes

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