Evidence map›Paper›PMID 38442859›Full record

ArticleJournal of the Royal Society, Interface2024

Predicting the long-term collective behaviour of fish pairs with deep learning.

Vaios Papaspyros, Ramón Escobedo, Alexandre Alahi, Guy Theraulaz, Clément Sire, Francesco Mondada

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Vaios PapaspyrosMobile Robotic Systems (Mobots) group, Institute of Electrical and Micro Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.ORCID 0000-0001-8257-1891
Ramón EscobedoCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université de Toulouse III - Paul Sabatier, 31062 Toulouse, France.ORCID 0000-0002-9945-0460
Alexandre AlahiVITA group, Civil Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.ORCID 0000-0002-5004-1498
Guy TheraulazCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université de Toulouse III - Paul Sabatier, 31062 Toulouse, France.ORCID 0000-0002-3062-4215
Clément SireLaboratoire de Physique Théorique, CNRS, Université de Toulouse III - Paul Sabatier, 31062 Toulouse, France.ORCID 0000-0003-4089-4013
Francesco MondadaMobile Robotic Systems (Mobots) group, Institute of Electrical and Micro Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.ORCID 0000-0001-8641-8704

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species

Indexed as

Deep LearningAnimalsFishesMachine LearningMass BehaviorMotioncollective behaviourcomplex system dynamicsdeep learningfish schoolmathematical modelssocial interactions

Identifiers

PMID38442859
PMCPMC10914514

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.