ArticleToxics2026
Exploring the Use of Machine Learning in Marine Biomonitoring: Assessing Nickel Exposure in
Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The sea urchin embryo represents a well-established model organism widely applied in embryo toxicity assays, ecotoxicological investigations, and biomonitoring studies. These analyses are primarily based on the morphological evaluation of embryos, which is conventionally carried out by experts through light microscopy. However, this approach is both time-intensive and inherently subjective, as the outcomes strongly depend on the evaluator's expertise and experience. With the increasing adoption of machine learning techniques in image classification tasks, this study investigates the applicability of machine-learning-based approaches for the classification of sea urchin embryo images. The dataset used in this work originates from a previous study examining the effects of nickel exposure on
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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.