ReviewBioscience2024
Exploring the landscape of automated species identification apps: Development, promise, and user appraisal.
Review in Bioscience, 2024. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Natural history in the Anthropocene.Bioscience · 2026Article
- Evaluation of the e-Surveyor Mobile Application for Undertaking Plant Surveys and Predicting Habitat Type.Ecology and evolution · 2026Article
- Evaluating species identification apps as a tool for small plot-based surveys of vascular plants in Alberta, Canada.AoB PLANTS · 2026Article
- Towards the automatized identification of moss species from their spore morphology.Annals of botany · 2026Article
- Review
- AI-Powered Plant Science: Transforming Forestry Monitoring, Disease Prediction, and Climate Adaptation.Plants (Basel, Switzerland) · 2025Review
- Socioeconomic stratification in adolescent digital engagement: cultural capital, emotional mediation, and bilibili usage patterns in Chinese high schools.Frontiers in sociology · 2025Article
- Exploring the landscape of automated species identification apps: Development, promise, and user appraisal.Bioscience · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Two decades ago, Gaston and O'Neill (2004) deliberated on why automated species identification had not become widely employed. We no longer have to wonder: This AI-based technology is here, embedded in numerous web and mobile apps used by large audiences interested in nature. Now that automated species identification tools are available, popular, and efficient, it is time to look at how the apps are developed, what they promise, and how users appraise them. Delving into the automated species identification apps landscape, we found that free and paid apps differ fundamentally in presentation, experience, and the use of biodiversity and personal data. However, these two business models are deeply intertwined. Going forward, although big tech companies will eventually take over the landscape, citizen science programs will likely continue to have their own identification tools because of their specific purpose and their ability to create a strong sense of belonging among naturalist communities.
Indexed as
Identifiers
What OpenQuestion holds
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.