{"id":"W4391691048","doi":"10.26434/chemrxiv-2024-cwnwc","title":"An affordable platform for automated synthesis and electrochemical characterization","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Mitacs; Canadian Institute for Advanced Research; University of Minnesota; U.S. Department of Energy","keywords":"Characterization (materials science); Electrochemistry; Computer science; Chemistry; Nanotechnology; Materials science; Electrode; Physical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009695222,0.0003707893,0.0004651354,0.0001228433,0.0001648816,0.0008547376,0.0006154185,0.000380971,0.000332342],"category_scores_gemma":[0.000381928,0.000336163,0.00007337013,0.0001196018,0.0001519474,0.0001995504,0.0005991581,0.0003125521,0.000102123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000979335,"about_ca_system_score_gemma":0.0001619423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001964974,"about_ca_topic_score_gemma":0.000001871422,"domain_scores_codex":[0.9977454,0.00004716437,0.0004090375,0.0010321,0.0002637727,0.000502511],"domain_scores_gemma":[0.9987923,0.0001518636,0.0002348176,0.0005672232,0.00009549215,0.000158284],"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":[0.00004000571,0.00003152163,0.00003320948,0.0009030204,0.000008186505,0.000001968639,0.0001569184,0.00007868312,0.9978735,0.0004163455,0.0001540297,0.0003026289],"study_design_scores_gemma":[0.00007227383,0.00004276492,0.0002380481,0.0001978839,0.00005660075,0.00001210119,0.000008705216,0.1765978,0.8186467,0.003389883,0.0003854522,0.0003517591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920285,0.00007016541,0.004100029,0.0002776153,0.001025854,0.0005696735,0.00006026498,0.001663645,0.0002042163],"genre_scores_gemma":[0.9774907,0.00002618704,0.02085987,0.00008309053,0.0004396965,0.0005297891,0.0002958883,0.00008402637,0.0001907885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1792268,"threshold_uncertainty_score":0.999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336306593386903,"score_gpt":0.2762290398453408,"score_spread":0.2628659739114717,"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."}}