Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Predicting sediment toxicity using logistic regression: a concentration-addition approach.

Eric P Smith1, Tim Robinson, L Jay Field

  • 1Department of Statistics, Virginia Tech, Blacksburg, Virginia 24061-0439, USA. epsmith@vt.edu

Environmental Toxicology and Chemistry
|March 12, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Targeting Hsp70 Immunosuppressive Signaling Axis with Lipid Nanovesicles: A Novel Approach to Treat Pancreatic Cancer.

Cancers·2025
Same author

Profiling the gut and oral microbiota of hormone receptor-positive, HER2-negative metastatic breast cancer patients receiving pembrolizumab and eribulin.

Microbiome research reports·2025
Same author

Correction: CREB1-BCL2 drives mitochondrial resilience in RAS GAP-dependent breast cancer chemoresistance.

Oncogene·2025
Same author

Exhaled carbon monoxide: variations due to collection method and physiology.

Journal of breath research·2025
Same author

TLR2-Bound Cancer-Secreted Hsp70 Induces MerTK-Mediated Immunosuppression and Tumorigenesis in Solid Tumors.

Cancers·2025
Same author

UK cancer vaccine advance - Recognising and realising opportunities.

Cambridge prisms. Precision medicine·2025

Chemical concentration measurements can predict sediment toxicity test outcomes with 77% accuracy using logistic regression models. While models show promise, predicting non-toxic samples is more reliable than predicting toxic ones.

Area of Science:

  • Environmental Chemistry
  • Ecotoxicology
  • Statistical Modeling

Background:

  • Sediment toxicity testing is crucial for environmental risk assessment.
  • Accurate prediction of toxicity test outcomes is needed for efficient risk management.
  • Chemical concentrations in sediment are potential predictors of toxicity.

Purpose of the Study:

  • To evaluate the utility of chemical concentration measurements for predicting sediment toxicity test results.
  • To develop and assess predictive models for sediment toxicity.

Main Methods:

  • Utilized matched data on sediment toxicity and chemical concentrations from multiple studies.
  • Employed multiple logistic regression and concentration-addition models.
  • Developed three distinct predictive models: individual chemical selection, derived variables for contaminant groups (metals, polycyclic aromatic hydrocarbons [PAHs]), and a separate species model.

Related Experiment Videos

Main Results:

  • Three logistic regression models met acceptability criteria for predicting sediment toxicity.
  • Models achieved approximately 77% overall prediction accuracy.
  • Prediction accuracy was higher for non-toxic samples compared to toxic samples.

Conclusions:

  • Chemical concentration data, when analyzed with appropriate statistical models, can be useful for predicting sediment toxicity.
  • The developed models offer a valuable tool for environmental risk assessment and management.
  • Further refinement may improve the prediction of toxic sediment samples.