{"id":"W4234732116","doi":"10.26434/chemrxiv.12798143","title":"Automated Liquid-Level Monitoring and Control using Computer Vision","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Advanced Research Projects Agency; Defense Advanced Research Projects Agency; University of British Columbia","keywords":"Computer science; Software deployment; Distillation; Process engineering; Swap (finance); Variety (cybernetics); Monitoring and control; Artificial intelligence; Control engineering; Software engineering; Engineering; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004432755,0.0004047798,0.000405474,0.0005960816,0.0002843541,0.0009969904,0.0007192146,0.0005787001,0.001626616],"category_scores_gemma":[0.0007638642,0.0002409448,0.0002647629,0.000377821,0.0005824754,0.0007281885,0.0006287933,0.0006632762,0.0005531349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007345554,"about_ca_system_score_gemma":0.0008745939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002676372,"about_ca_topic_score_gemma":0.00300651,"domain_scores_codex":[0.9994042,0.00005729251,0.00002280245,0.0001919202,0.0002677193,0.00005608983],"domain_scores_gemma":[0.9996678,0.0001052985,0.00005212457,0.00006590847,0.00008427339,0.00002472822],"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":[0.0001336019,0.0001863196,0.001102964,0.0001005193,0.00003790854,0.00007804103,0.00009023766,0.01525582,0.7623803,0.003066331,0.002020496,0.2155474],"study_design_scores_gemma":[0.000072096,0.000280785,0.003859798,0.00001988175,0.00002658559,0.0001868695,0.00003051718,0.4283527,0.552125,0.002857985,0.01212308,0.0000649081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05546356,0.0002769924,0.9342922,0.000217942,0.00004617034,0.0001894745,0.0001277803,0.005525442,0.003860431],"genre_scores_gemma":[0.4784303,0.0002885603,0.5171632,0.0001582699,0.00004096348,0.0001916335,0.0002472307,0.0001602609,0.003319577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002676372,"threshold_uncertainty_score":0.005441546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03534405588391407,"score_gpt":0.2838951419810823,"score_spread":0.2485510860971683,"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."}}