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Record W4412578017 · doi:10.1111/ejn.70201

CARE Competition “Exploring the Necessity of Animal Experiments”

2025· editorial· en· W4412578017 on OpenAlexaboutno aff
Christelle Baunez

Bibliographic record

VenueEuropean Journal of Neuroscience · 2025
Typeeditorial
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)PsychologyData scienceNeuroscienceComputer scienceBiologyEcology

Abstract

fetched live from OpenAlex

Within FENS, the CARE committee was originally created to better inform the neuroscientific community and homogenize the actions taken to improve animal well-being in our research and apply the 3Rs (Refine, Reduce, Replace). In parallel, however, the actions taken against animal use in research forced the European parliament to take action and re-evaluate animal experimentation. As CARE committee, we have thus engaged in activities to better coordinate with other organizations (such as EARA [European Association on Research using Animals], EBC [European Brain Council], CAR of SFN) to meet and inform politicians, the public, and the scientific community at large, regarding the continued need to use animals in research. Our message is that animal experiments are an essential part of progress in neuroscience and are being done with great responsibility and maximal care to reduce harm and improve well-being, in line with the 3Rs. In neuroscience, nonanimal methods currently complement animal experiments, as each model offers unique strengths for addressing different aspects of research questions. However, phasing out animal research remains on the agenda and it is important that the next generation of scientists is prepared to take responsibility and advocate for the use of animals in brain research for as long as necessary. In order to make this generation of scientists share their reflections about this often highly emotional topic, the CARE committee launched a writing competition under the motto: “Why do we still need animal research in neurosciences?”. Two categories of assays could be submitted “scientific assays” or “literary assays”. The call for participation was launched via the FENS newsletter, our CARE webpage (https://www.fens.org/about-fens/our-organisation/fens-committees/care-committee) and various social media platforms (Facebook, X, LinkedIn etc.). A total of 43 entries were received from 23 different countries as diverse as Canada, India, Nigeria, Denmark, Ireland, and so on. All members of CARE acted as member of the jury and all converged towards the same impression that all assays reflected a deep thinking and a great sense of responsibility from each participant. One winner and two runners-up of each category have been nominated and are published in the FENS official journal EJN as a special issue. The two winners also received a €500 prize, and their text was highlighted at the FENS Forum 2024 in Vienna. The winner of the best scientific assay is Cassandre Vielle (current PhD student in ethology at Concordia University, Montreal, Canada; holder of a PhD in Neurosciences from Aix-Marseille University, France). She has written an assay on the use of animals in drug-addiction research and how it could be improved for better translatability towards human patients suffering from addiction. The winner of the literary assay is Ivan Montiel (current post-doc at Institut Pasteur, Paris, France). He has written an assay entitled “Beyond the Use: The Paradox of Scientific Animal Utilization” in which he describes the difficult guilty feelings after his mice were sacrificed, while wandering around in the Paris streets, leaving the Institut Pasteur. We will also have the opportunity to discover the runners-up of each category. For the scientific essay, Ana Isabel Guimarães, a Master student from University of Porto, Portugal, explores the disadvantages of animal models and considers alternatives in a balanced way. Ingvild Bjerke, PhD student from University of Oslo, Norway, suggests in her essay that we change our routines to increase data-sharing and reuse in order to reduce the number of animals used. Finally, the two runners-up of the literary essays are Lene-Marlen Wessel, Master's student from University of Copenhagen, Denmark, who took alternating perspectives of a laboratory rat and a student on the day of sacrifice to lead us to a moving discussion on “useful death”. The second runner-up of the literary category is Rand Suleimann, Master's student from the University of Dresden, Germany, who places a young neurobiologist in front of her professor, questioning her about animal use in her research and then initiating a complete change in her research. Because all these questions raised by young scientists are timely and require deep thought, we can only encourage you to read these essays and motivate your early-career collaborators to think about the need of animals in neuroscience research and consider participation in the next competition in 2026 for the FENS Forum in Barcelona. The author declares no conflicts of interest. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ejn.70201.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.262
metaresearch head score (Gemma)0.213
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.262
Threshold uncertainty score0.909

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2620.213
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.002
Science and technology studies0.0170.033
Scholarly communication0.0150.010
Open science0.0120.023
Research integrity0.0280.040
Insufficient payload (model declined to judge)0.0230.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.200
GPT teacher head0.396
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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