{"id":"W2995901685","doi":"10.7554/elife.49630","title":"MouseBytes, an open-access high-throughput pipeline and database for rodent touchscreen-based cognitive assessment","year":2019,"lang":"en","type":"article","venue":"eLife","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Baycrest Hospital; University of Guelph; Western University","funders":"Weston Brain Institute; Canada First Research Excellence Fund; Canada Research Chairs; Canadian Institutes of Health Research; Mitacs; Alzheimer Society; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Canadian Institute for Advanced Research","keywords":"Dissemination; Open science; Touchscreen; Computer science; Cognition; Metadata; Open data; Throughput; Database; World Wide Web; Data science; Human–computer interaction; Biology","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":[0.0004768917,0.0002226093,0.0003032082,0.00008006342,0.0003019144,0.0004247966,0.0005969756,0.00004358378,0.0001528328],"category_scores_gemma":[0.003350115,0.0002070147,0.00003258345,0.0001914808,0.0001140312,0.001337182,0.0009096937,0.0001461336,0.00004086376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007086093,"about_ca_system_score_gemma":0.0001796571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004478315,"about_ca_topic_score_gemma":0.0003418267,"domain_scores_codex":[0.9978684,0.0001559385,0.0002309603,0.0009835387,0.0004483498,0.0003128567],"domain_scores_gemma":[0.9946051,0.004530204,0.0001303226,0.0004214811,0.0001845735,0.0001283106],"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":[0.006958962,0.007877301,0.1215457,0.0009795764,0.0002769456,0.0001434089,0.0006679856,0.004279132,0.5460339,0.0664569,0.2184192,0.02636109],"study_design_scores_gemma":[0.01732298,0.003680544,0.08660228,0.0003188887,0.0001712739,0.00001951905,0.0006604049,0.07836139,0.7307387,0.001097293,0.07923037,0.00179638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452962,0.00008891765,0.03863677,0.00797628,0.0009941785,0.003148915,0.002061425,0.0001813828,0.001615956],"genre_scores_gemma":[0.9786795,0.00003118426,0.002797775,0.01704493,0.000201309,0.0002973492,0.0001441939,0.00003993351,0.0007638357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1847048,"threshold_uncertainty_score":0.8441815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1139974748662608,"score_gpt":0.4073310365471821,"score_spread":0.2933335616809213,"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."}}