ArticleRoyal Society open science2023
Recognizability bias in citizen science photographs.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Machine learning training data: over 500,000 images of butterflies and moths (Lepidoptera) with species labels.Scientific data · 2025Article
- First national survey of terrestrial biodiversity using airborne eDNA.Scientific reports · 2025Article
- Review
- Bridging Citizen Science and Expert Surveys in urban biodiversity monitoring: Insights from insect diversity in Macao.Biodiversity data journal · 2025Article
- Emerging technologies in citizen science and potential for insect monitoring.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Article
- Recognizability bias in citizen science photographs.Royal Society open science · 2023Article
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
Citizen science and automated collection methods increasingly depend on image recognition to provide the amounts of observational data research and management needs. Recognition models, meanwhile, also require large amounts of data from these sources, creating a feedback loop between the methods and tools. Species that are harder to recognize, both for humans and machine learning algorithms, are likely to be under-reported, and thus be less prevalent in the training data. As a result, the feedback loop may hamper training mostly for species that already pose the greatest challenge. In this study, we trained recognition models for various taxa, and found evidence for a
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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.