Effect of Two Shark-Repelling Methods on the Seawater Quality and Acute Toxicity of Seawater Fish
Li'na LIU; Jinjin WANG; Meijie LIAO; Shifeng WANG; Bin LI; Xiaojun RONG; Yingeng WANG; Tongxiao ZHENG · 2023 · Progress in Fishery Sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
In this study, two kinds of shark repellents, chemical shark repellent and electric pulse shark-repelling device were selected to test their effects on seawater quality as well as the physiology and survival of fish using simulated ecology experiments. Three marine fish species, Japanese flounder (Paralichthys olivaceus), black rockfish (Sebastes schlegelii), and spotted grouper (Oplegnathus punctatus) were selected as test subjects. The results of water quality indicators showed that the chemical shark repellent could significantly reduce the water transparency and pH (P < 0.05), while the el
Abstract by Li'na LIU; Jinjin WANG; Meijie LIAO; Shifeng WANG; Bin LI; Xiaojun RONG; Yingeng WANG; Tongxiao ZHENG, Progress in Fishery Sciences (2023) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.19663/j.issn2095-9869.20220607002
