{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006869985,0.0006821914,0.000609557,0.001051468,0.000449502,0.0003464755,0.001362878,0.0006641482,0.006030317],"category_scores_gemma":[0.001427994,0.0004501842,0.0003729631,0.000465151,0.0003024481,0.0006430681,0.000688218,0.0005659029,0.001738518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005445941,"about_ca_system_score_gemma":0.0008096872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001558745,"about_ca_topic_score_gemma":0.003410961,"domain_scores_codex":[0.9995236,0.00005297182,0.00003973495,0.0001757096,0.0001584939,0.00004949435],"domain_scores_gemma":[0.9990643,0.0002060397,0.0001699874,0.0002021619,0.000261455,0.0000962054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007013433,0.0004789615,0.00443554,0.0002823502,0.00005588027,0.0001446135,0.0001750434,0.003379479,0.6899136,0.0009406492,0.007971498,0.291521],"study_design_scores_gemma":[0.000322258,0.002669672,0.07345581,0.0001128349,0.0002410921,0.001685335,0.00005844159,0.1721525,0.6843307,0.001280421,0.06329574,0.0003952793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1533026,0.0002758612,0.8111208,0.0002314973,0.0001988037,0.001725873,0.001695207,0.02836814,0.003081216],"genre_scores_gemma":[0.3118354,0.000263226,0.6723785,0.0003373253,0.0001257542,0.00323003,0.001432799,0.001221351,0.009175561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006030317,"threshold_uncertainty_score":0.02017343,"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."}}