ArticleiScience2020
Automatic Classification of Cichlid Behaviors Using 3D Convolutional Residual Networks.
Article in iScience, 2020. 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
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
7 citing papers in PubMed, 17 citations in OpenAlex.
- Semi-automated detection of cleaning interactions using supervised machine learning.Scientific reports · 2026Article
- SARTAB, a scalable system for automated real-time behavior detection based on animal tracking and Region Of Interest analysis: validation on fish courtship behavior.Frontiers in behavioral neuroscience · 2024Article
- SMART-BARN: Scalable multimodal arena for real-time tracking behavior of animals in large numbers.Science advances · 2023Article
- Cellular profiling of a recently-evolved social behavior in cichlid fishes.Nature communications · 2023Article
- Indirect Genetic Effects: A Cross-disciplinary Perspective on Empirical Studies.The Journal of heredity · 2022Article
- Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning.Animals : an open access journal from MDPI · 2021Article
- Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Many behaviors that are critical for survival and reproduction are expressed over extended time periods. The ability to inexpensively record and store large volumes of video data creates new opportunities to understand the biological basis of these behaviors and simultaneously creates a need for tools that can automatically quantify behaviors from large video datasets. Here, we demonstrate that 3D Residual Networks can be used to classify an array of complex behaviors in Lake Malawi cichlid fishes. We first apply pixel-based hidden Markov modeling combined with density-based spatiotemporal clustering to identify sand disturbance events. After this, a 3D ResNet, trained on 11,000 manually annotated video clips, accurately (>76%) classifies the sand disturbance events into 10 fish behavior categories, distinguishing between spitting, scooping, fin swipes, and spawning. Furthermore, animal intent can be determined from these clips, as spits and scoops performed during bower construction are classified independently from those during feeding.
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.