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Record W2767549149 · doi:10.51291/2377-7478.1242

Learning, memory, cognition, and the question of sentience in fish

2017· article· en· W2767549149 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnimal Sentience · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSentienceFish <Actinopterygii>VertebrateTuring testVariety (cybernetics)PsychologyCognitionCognitive scienceTheory of mindCognitive psychologyBiologyEpistemologyComputer scienceNeuroscienceArtificial intelligencePhilosophyFishery

Abstract

fetched live from OpenAlex

Evolutionarily conserved features have been demonstrated at many levels of biological organization across a variety of species. Evolutionary conservation may apply to complex behavioral phenomena too. It is thus not inconceivable that a form of sentience does exist even in the lowest order vertebrate taxon, the teleosts. How similar it is to human sentience in its level of complexity or in its multidimensional features is a difficult question, especially from an experimental standpoint, given that even the definition of human sentience is debated. Woodruff attempts a Turing-like test of fish sentience, and lists numerous neuroanatomic, neurophysiological and behavioral similarities between fish and humans. In this commentary, I add to these similarities by discussing empirical findings showing complex forms of mental representation in fish. At the same time, I note that without a more thorough understanding of human sentience and more data on similarities as well as differences between fish and mammals, the final conclusion may have to wait.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.314
Teacher spread0.303 · 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