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Record W4286467439 · doi:10.1093/conphys/coac047

Swimming performance of a pelagic species in the Yangtze River under different exposure modes of the total dissolved gas supersaturation

2022· article· en· W4286467439 on OpenAlexaff
Qianfeng Ji, Kefeng Li, Yuanming Wang, Ruifeng Liang, Jingjie Feng, Ran Li, David Z. Zhu

Bibliographic record

VenueConservation Physiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Alberta
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsPelagic zoneBiologySupersaturationFisheryAnimal scienceEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

During flood discharges of upstream dams in the Yangtze River, the pelagic fish have a stress risk from total dissolved gas (TDG) supersaturation in the river water. This study took the silver carp as the object and systematically evaluated the effects of TDG supersaturation levels and exposure time on their critical swimming speed (Ucrit) at different temperatures. The external symptoms of gas bubble disease were found when TDG levels exceeded 130%. Both exposure time and TDG level did not significantly impact the Ucrit of fish under 6 days of non-lethal exposure (110%, 120%, 130% TDG) with lower or higher water temperature. Significant differences in Ucrit were found among different exposure times at 11.0 ± 1.0°C under 10 hours of lethal exposure (135%, 140%, 150% TDG) and the Ucrit reduced by 59.88%, 83.32%, and 92.40%, respectively. TDG level had a significant impact on the Ucrit at 21.0 ± 1.0°C when exposure time exceeded 8 hours. Ucrit at 21.0 ± 1.0°C water were significantly greater than those at 11.0 ± 1.0°C water where conditions had the same TDG supersaturation and exposure time. Differences in Ucrit between temperatures ranged from 3.24 to 6.12 BL/s under non-lethal exposure and from 6.38 to 13.88 BL/s under lethal exposure. The results of this study can provide a reference for fish conservation during flood discharge.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.309

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)0.0000.000

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 head0.013
GPT teacher head0.188
Teacher spread0.175 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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