{"id":"W4244134325","doi":"10.26434/chemrxiv.13198688","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":0,"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 (computing); Process engineering; Chemistry; Engineering; Robot; Operating system; 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":[0.0009388166,0.0007319071,0.0007853545,0.001319972,0.0004327459,0.001088783,0.001307936,0.001016875,0.007728093],"category_scores_gemma":[0.001001164,0.0004749951,0.0005991543,0.0004635188,0.0004917774,0.0008128812,0.0009133967,0.0008632137,0.004426018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008444348,"about_ca_system_score_gemma":0.001375861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002535579,"about_ca_topic_score_gemma":0.002372612,"domain_scores_codex":[0.9988656,0.00007700965,0.00004545259,0.0003773469,0.0005422423,0.00009227737],"domain_scores_gemma":[0.9993535,0.0001097986,0.00006149619,0.00008988335,0.000334485,0.0000508104],"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":[0.0003151287,0.0003439388,0.0007996503,0.0002100369,0.00004664579,0.0001374371,0.00007088155,0.005757126,0.8394763,0.001930819,0.009278552,0.1416335],"study_design_scores_gemma":[0.00008906706,0.0005886845,0.002717624,0.00002430441,0.00003533598,0.0002531483,0.00003261558,0.2844264,0.6856588,0.001315858,0.02472508,0.000133061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07988778,0.0005352957,0.8721492,0.0004036285,0.0002970538,0.001083201,0.002218009,0.03225761,0.0111682],"genre_scores_gemma":[0.1937986,0.0003355947,0.7901313,0.0004619884,0.00007360366,0.001415728,0.002773441,0.0008000784,0.01020974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007728093,"threshold_uncertainty_score":0.0258531,"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."}}