ArticleScientific reports2020
A new method to control error rates in automated species identification with deep learning algorithms.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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
12 citing papers in PubMed.
- Developing custom computer vision models with Njobvu-AI: A collaborative, user-friendly platform for ecological research.Ecological applications : a publication of the Ecological Society of America · 2025Article
- Expanding phenological insights: automated phenostage annotation with community science plant images.International journal of biometeorology · 2025Article
- Impact of deep learning and post-processing algorithms performances on biodiversity metrics assessed on videos.PloS one · 2025Article
- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- Review
- Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring.Frontiers in plant science · 2023Article
- Errors in aerial survey count data: Identifying pitfalls and solutions.Ecology and evolution · 2022Review
- Leveraging social media and deep learning to detect rare megafauna in video surveys.Conservation biology : the journal of the Society for Conservation Biology · 2022Article
- Automatic detection of fish and tracking of movement for ecology.Ecology and evolution · 2021Article
- Article
- Article
- Deep learning in deep time.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Processing data from surveys using photos or videos remains a major bottleneck in ecology. Deep Learning Algorithms (DLAs) have been increasingly used to automatically identify organisms on images. However, despite recent advances, it remains difficult to control the error rate of such methods. Here, we proposed a new framework to control the error rate of DLAs. More precisely, for each species, a confidence threshold was automatically computed using a training dataset independent from the one used to train the DLAs. These species-specific thresholds were then used to post-process the outputs of the DLAs, assigning classification scores to each class for a given image including a new class called "unsure". We applied this framework to a study case identifying 20 fish species from 13,232 underwater images on coral reefs. The overall rate of species misclassification decreased from 22% with the raw DLAs to 2.98% after post-processing using the thresholds defined to minimize the risk of misclassification. This new framework has the potential to unclog the bottleneck of information extraction from massive digital data while ensuring a high level of accuracy in biodiversity assessment.
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.