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Record W2123631236

anyFish: an open-source software to generate animated fish models for behavioural studies

2013· article· en· W2123631236 on OpenAlexaff
Thor Veen, Spencer J. Ingley, Rongfeng Cui, Jon Simpson, Mohammad Rahmani Asl, Ji Zhang, Trisha Butkowski, Li Wen, Chelsea Hash, Jerald B. Johnson, Wei Yan, Gil G. Rosenthal

Bibliographic record

VenueEvolutionary ecology research · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSticklebackSoftwareComputer scienceFish <Actinopterygii>Selection (genetic algorithm)Identification (biology)Human–computer interactionIsolation (microbiology)Artificial intelligenceBiologyEcologyFisheryBioinformatics
DOInot available

Abstract

fetched live from OpenAlex

Problem: Using an experimental approach to study behaviours based on visual signals is severely limited due to the difficulty of combining realistic models (e.g. live fish) with the manipulation of signals in isolation. Solution: Computer animations allow the manipulation of a single cue while maintaining the rest of the behavioural phenotype of a realistic three-dimensional (3D) model. Software: We introduce the open-source software anyFish for the creation of 3D-animated fish. Both the animated model and its behaviour can be modified by the end-user to suit specific needs. Applications: Computer-animated fish facilitate the identification of factors influencing behaviours based on visual cues, and ultimately the way they both drive and respond to selection. For our research, we vary nuptial colour and size and shape of animated male stickleback to quantify female choice for these characters. The software has many other applications, as other fish species can be animated and characters like swimming speed and direction can be manipulated as well.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.274
GPT teacher head0.395
Teacher spread0.121 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2013
Admission routes1
Has abstractyes

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