{"id":"W3084483561","doi":"10.1021/acscentsci.0c00460","title":"Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set","year":2020,"lang":"en","type":"article","venue":"ACS Central Science","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Defense Advanced Research Projects Agency","keywords":"Artificial intelligence; Computer science; Set (abstract data type); Convolutional neural network; Task (project management); Data set; Segmentation; Support vector machine; Pattern recognition (psychology); Machine learning; Computer vision; Engineering","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":[0.001387944,0.002394292,0.0009615417,0.002691348,0.0006448283,0.001232913,0.002625496,0.001959814,0.00675956],"category_scores_gemma":[0.002501204,0.0005458099,0.001636854,0.002816166,0.0008531237,0.00190748,0.001505203,0.002293455,0.00743559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665104,"about_ca_system_score_gemma":0.001426138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01652025,"about_ca_topic_score_gemma":0.01893991,"domain_scores_codex":[0.9984161,0.0002260021,0.0001339609,0.0004438731,0.0005340948,0.0002459472],"domain_scores_gemma":[0.9985074,0.0002644304,0.0001200796,0.0005042516,0.0004415088,0.0001622887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002372133,0.003947244,0.01768372,0.002417353,0.000428444,0.00104932,0.0002353768,0.05735979,0.03912197,0.004553676,0.4185934,0.4522374],"study_design_scores_gemma":[0.0006415732,0.001727878,0.09714902,0.0002902074,0.000189931,0.001900275,0.0007591781,0.4530596,0.1065339,0.008389899,0.3289958,0.0003627728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3354579,0.004642889,0.08204781,0.002270665,0.001434987,0.002904365,0.4969668,0.03942202,0.03485252],"genre_scores_gemma":[0.1875842,0.0007930309,0.07701719,0.0003815926,0.000112762,0.001224924,0.7250975,0.0005639223,0.007224851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01652025,"threshold_uncertainty_score":0.03284818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02768111228597971,"score_gpt":0.2536242268581728,"score_spread":0.225943114572193,"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."}}