{"id":"W4385215039","doi":"10.32920/23737437.v1","title":"CageView: A Smart Food Control and Monitoring System for Phenotypical Research In Vivo","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Toronto Metropolitan University","funders":"","keywords":"Schedule; Computer science; Convolutional neural network; Real-time computing; Set (abstract data type); Control (management); Histogram; Artificial intelligence; Embedded system; Operating system","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":[0.0007603858,0.0005868157,0.0005281309,0.0006683169,0.0002178952,0.0004778215,0.00137771,0.0007223299,0.009309709],"category_scores_gemma":[0.0011136,0.0004393378,0.0004509683,0.0002674974,0.0004200123,0.0006929272,0.0009779615,0.0006170465,0.002178449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003347037,"about_ca_system_score_gemma":0.0004986093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009578514,"about_ca_topic_score_gemma":0.001991485,"domain_scores_codex":[0.9995731,0.00005663384,0.0000195487,0.000150157,0.0001710336,0.00002959636],"domain_scores_gemma":[0.9992762,0.0001691198,0.0001105372,0.0001844318,0.0001362741,0.0001234499],"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":[0.0004937946,0.0001524277,0.004617675,0.0002075588,0.00006332589,0.0002073206,0.0001121027,0.001922359,0.8142824,0.0014894,0.01523422,0.1612175],"study_design_scores_gemma":[0.0002365228,0.002094715,0.04674307,0.000134045,0.0001820251,0.002480115,0.0001258003,0.09136005,0.7071332,0.004055298,0.1451673,0.0002878507],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04925642,0.0004207345,0.9125815,0.0003022156,0.0002879217,0.0003859651,0.004150816,0.02803089,0.004583591],"genre_scores_gemma":[0.167077,0.0005864238,0.8093403,0.0007133454,0.0001034287,0.002142281,0.00480263,0.002838493,0.01239618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009309709,"threshold_uncertainty_score":0.03114408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2186615725810479,"score_gpt":0.3777538386325475,"score_spread":0.1590922660514996,"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."}}