{"id":"W4396629696","doi":"10.2139/ssrn.4815518","title":"An Affordable Platform for Automated Synthesis and Electrochemical Characterization","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Characterization (materials science); Electrochemistry; Computer science; Business; Nanotechnology; Chemistry; Materials science; Electrode","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004745395,0.0004520068,0.0005456606,0.0002453428,0.0003986346,0.001270064,0.0008869992,0.0003911786,0.0001206503],"category_scores_gemma":[0.0003739031,0.0003914857,0.0001272763,0.000143323,0.0001257532,0.000353906,0.0004319366,0.002337534,0.00005383883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009281135,"about_ca_system_score_gemma":0.002681846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000428459,"about_ca_topic_score_gemma":0.00005201949,"domain_scores_codex":[0.9952399,0.0001499343,0.0006191463,0.0008576248,0.000445533,0.002687819],"domain_scores_gemma":[0.9986249,0.0001449117,0.0004777851,0.000409392,0.0001550989,0.0001879277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001153727,0.00004208895,0.00002997598,0.0002240473,0.00004001778,0.000002232245,0.0001367637,0.0002483249,0.9843747,0.01269939,0.00002979782,0.002057271],"study_design_scores_gemma":[0.0004123858,0.0007079889,0.0001916548,0.0004576404,0.0003122294,0.001088776,0.0002069468,0.2409219,0.3267265,0.4273272,0.0005467876,0.001100046],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961072,0.0005904732,0.03512647,0.0005364005,0.001230162,0.0005088347,0.0000796196,0.0007871652,0.00006887731],"genre_scores_gemma":[0.9913593,0.0008723309,0.006045149,0.00006459042,0.0009269962,0.000168809,0.000117451,0.0001109759,0.0003343939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6576482,"threshold_uncertainty_score":0.9999641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008242495415280032,"score_gpt":0.270306491902627,"score_spread":0.262063996487347,"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."}}