{"id":"W2189068597","doi":"10.7759/cureus.397","title":"An Automated Motion Detection and Reward System for Animal Training","year":2015,"lang":"en","type":"article","venue":"Cureus","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bishop's University","funders":"National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health","keywords":"Trainer; Functional magnetic resonance imaging; Session (web analytics); Neuroimaging; Stimulus (psychology); Training system; Computer science; Medicine; Software; Computer hardware; Human–computer interaction; Artificial intelligence; Cognitive psychology; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000299225,0.0000844629,0.0001051439,0.00003877472,0.0001909731,0.00004573981,0.00005643092,0.0000477803,6.796716e-7],"category_scores_gemma":[0.003047775,0.00008178491,0.00002000161,0.0001039028,0.00004389048,0.0003054194,0.0000205978,0.00004557959,0.000005573289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001172527,"about_ca_system_score_gemma":0.00002183738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001962301,"about_ca_topic_score_gemma":0.00003580087,"domain_scores_codex":[0.9992325,0.00007821439,0.0000977265,0.0003110393,0.0001324359,0.0001480461],"domain_scores_gemma":[0.9992316,0.0004619637,0.0000492111,0.0001109484,0.0000685165,0.00007771539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003022187,0.00006315813,0.0004173632,0.00008107139,0.00001414953,0.000007171045,0.002979317,0.0002506472,0.9837139,0.002870545,0.001005975,0.008294478],"study_design_scores_gemma":[0.002947755,0.003886971,0.01604638,0.00007074782,0.00007057227,0.0004100838,0.009567221,0.5447624,0.4131526,0.001458989,0.006974218,0.0006519995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907504,0.00005390996,0.006065473,0.0003807571,0.0008041614,0.0003047847,0.00001571826,0.001028041,0.0005967274],"genre_scores_gemma":[0.9993106,0.000001132763,0.0002970593,0.0001274639,0.0001769637,0.00005321379,0.000001171357,0.00001356009,0.00001887427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5705612,"threshold_uncertainty_score":0.3648692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1198707094985843,"score_gpt":0.3208308963800472,"score_spread":0.2009601868814629,"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."}}