{"id":"W2969408089","doi":"10.1073/pnas.1909985116","title":"Optimizing organic electrosynthesis through controlled voltage dosing and artificial intelligence","year":2019,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"Electrosynthesis; Biochemical engineering; Process (computing); Computer science; Nanotechnology; Process engineering; Electrochemistry; Chemistry; Materials science; Engineering; Electrode","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000291735,0.0004139087,0.0001960129,0.0002248705,0.0001417691,0.0005722528,0.0003358451,0.0002736682,0.000808025],"category_scores_gemma":[0.0005220909,0.0001940399,0.0001459754,0.0002907496,0.0004339675,0.0007637411,0.0003533431,0.0005671997,0.0001841049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003256062,"about_ca_system_score_gemma":0.0001945184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001764966,"about_ca_topic_score_gemma":0.0005985915,"domain_scores_codex":[0.9998509,0.00001622939,0.00001544902,0.00004132731,0.00005557577,0.00002049662],"domain_scores_gemma":[0.9998708,0.00006173644,0.00003624725,0.00001247086,0.00001307167,0.000005680628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008075615,0.00007999103,0.0002071812,0.0002522125,0.00001240026,0.00005731921,0.00003938301,0.00672585,0.9601068,0.002926074,0.0001364652,0.02937549],"study_design_scores_gemma":[0.00003009438,0.0002979216,0.0005363802,0.00001545424,0.00001046689,0.00005991773,0.00003205584,0.0346842,0.9586642,0.001342309,0.004309705,0.00001731894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.830664,0.004478541,0.1482427,0.0005011238,0.0001350701,0.0002481314,0.0001898419,0.000559104,0.01498148],"genre_scores_gemma":[0.9524726,0.001945121,0.04413975,0.00009012136,0.00002124665,0.0001101619,0.0000819335,0.00005174395,0.001087239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000808025,"threshold_uncertainty_score":0.00270313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572579179192248,"score_gpt":0.2694861696479645,"score_spread":0.243760377856042,"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."}}