{"id":"W4248721249","doi":"10.26434/chemrxiv.13198688.v1","title":"Automated Solubility Screening Platform Using Computer Vision","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Resources Canada; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; University of British Columbia; Amgen; U.S. Department of Defense","keywords":"Automation; Solubility; Robotics; Computer science; Artificial intelligence; Process engineering; Robot; Engineering; Chemistry; Mechanical engineering","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":[0.0008368025,0.0007173103,0.0008233876,0.001197307,0.0003807409,0.0009785785,0.001270931,0.001012255,0.006111066],"category_scores_gemma":[0.00090079,0.000396394,0.0005991305,0.0004490641,0.0004584914,0.0007852468,0.000858631,0.0008707643,0.003345466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007164188,"about_ca_system_score_gemma":0.001248306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001982318,"about_ca_topic_score_gemma":0.001759947,"domain_scores_codex":[0.9989892,0.00007046067,0.00003871028,0.000319151,0.000494249,0.0000882328],"domain_scores_gemma":[0.9994567,0.00009296026,0.00005497679,0.00007108666,0.0002820453,0.00004231263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003878984,0.0004249799,0.0009678487,0.0002834339,0.00005648903,0.0001565354,0.0000748214,0.009547807,0.8283904,0.002322704,0.007834161,0.149553],"study_design_scores_gemma":[0.00009148487,0.0007290427,0.002648724,0.00002769004,0.00003876536,0.0002630668,0.00003410926,0.3516073,0.6209393,0.001664988,0.02182842,0.0001270614],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08515053,0.000661429,0.8792153,0.0003523031,0.0002779069,0.0009957745,0.001848647,0.02216719,0.009330972],"genre_scores_gemma":[0.2446147,0.00044639,0.7412527,0.0004274414,0.00007708748,0.001398333,0.002647794,0.0005687515,0.00856686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006111066,"threshold_uncertainty_score":0.0204435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648918409403555,"score_gpt":0.2906446481959425,"score_spread":0.2441554641019069,"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."}}