ArticleEnvironmental monitoring and assessment2011
Water quality assessment by means of HFNI valvometry and high-frequency data modeling.
Article in Environmental monitoring and assessment, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Freshwater Mussels as Sentinels for Safe Drinking Water Supply in Europe.ACS ES&T water · 2023Article
- Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Article
- Monitoring Bivalve Behavior and Physiology in the Laboratory and Field Using Open-Source Tools.Integrative and comparative biology · 2022Article
- The sense of hearing in the Pacific oyster, Magallana gigas.PloS one · 2017Article
- Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry.Environmental monitoring and assessment · 2016Article
- Sensory determinants of valve rhythm dynamics provide in situ biodetection of copper in aquatic environments.Environmental science and pollution research international · 2016Article
- Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection.The ISME journal · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high-frequency measurements of valve activity in bivalves (e.g., valvometry) over a long period of time and in various environmental conditions allow a very accurate study of their behaviors as well as a global analysis of possible perturbations due to the environment. Valvometry uses the bivalve's ability to close its shell when exposed to a contaminant or other abnormal environmental conditions as an alarm to indicate possible perturbations in the environment. The modeling of such high-frequency serial valvometry data is statistically challenging, and here, a nonparametric approach based on kernel estimation is proposed. This method has the advantage of summarizing complex data into a simple density profile obtained from each animal at every 24-h period to ultimately make inference about time effect and external conditions on this profile. The statistical properties of the estimator are presented. Through an application to a sample of 16 oysters living in the Bay of Arcachon (France), we demonstrate that this method can be used to first estimate the normal biological rhythms of permanently immersed oysters and second to detect perturbations of these rhythms due to changes in their environment. We anticipate that this approach could have an important contribution to the survey of aquatic systems.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.