{"id":"W3119145828","doi":"10.3389/fnbeh.2020.570704","title":"Elemental and Configural Associative Learning in Spatial Tasks: Could Zebrafish be Used to Advance Our Knowledge?","year":2020,"lang":"en","type":"article","venue":"Frontiers in Behavioral Neuroscience","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Fondation Fyssen","keywords":"Spatial learning; Associative learning; Association (psychology); Zebrafish; Perspective (graphical); Simplicity; Relevance (law); Cognitive science; Computer science; Associative property; Psychology; Cognitive psychology; Neuroscience; Artificial intelligence; Biology; Cognition","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002122761,0.0001409159,0.0001736204,0.00008512313,0.0000882076,0.00004952442,0.0003584028,0.00009097481,0.000003176539],"category_scores_gemma":[0.000586345,0.0001540443,0.0000293439,0.0005139387,0.0001775059,0.00001918959,0.0002719861,0.0003287238,0.00000229789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006313012,"about_ca_system_score_gemma":0.0001182311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005339342,"about_ca_topic_score_gemma":0.0003167799,"domain_scores_codex":[0.9983217,0.0001141546,0.0002184771,0.0006144887,0.0002988515,0.0004323195],"domain_scores_gemma":[0.9994397,0.000009180994,0.00005879223,0.0001277785,0.00004187261,0.0003226455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006906758,0.0001696565,0.264071,0.000007110847,0.000001013031,0.00001604932,0.0004750529,0.00003315892,0.720359,0.000006925892,0.007832319,0.006959558],"study_design_scores_gemma":[0.003715912,0.003428269,0.6735066,0.00005087809,0.00001626454,0.00000650823,0.004237456,0.006011342,0.1812125,0.00003276873,0.1268151,0.0009664216],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893535,0.00005580564,0.004588218,0.005142933,0.0001998147,0.0004835633,0.0001010299,0.00001753611,0.0000576384],"genre_scores_gemma":[0.9972979,0.00003214054,0.0009258564,0.001425662,0.00005625551,0.00008630145,0.00004659084,0.00001456306,0.0001147428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5391465,"threshold_uncertainty_score":0.6281747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03512098061257663,"score_gpt":0.3531454620890291,"score_spread":0.3180244814764525,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}