{"id":"W4390970411","doi":"10.26434/chemrxiv-2024-rj946","title":"Self-Driving Laboratories for Chemistry and Materials Science","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Mitacs; Bundesministerium für Bildung und Forschung; Government of Ontario; Canada First Research Excellence Fund; Schmidt Family Foundation","keywords":"Automation; Computer science; Nanotechnology; Domain (mathematical analysis); Data science; Drug discovery; Chemistry; Engineering; Materials science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009161766,0.0002445036,0.0003685,0.000168751,0.0003393193,0.005841263,0.001911784,0.0001223066,0.00009241688],"category_scores_gemma":[0.004149332,0.0001924059,0.00006749554,0.0009897153,0.0004228137,0.0001497168,0.008018774,0.0001982376,0.00009370249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007649244,"about_ca_system_score_gemma":0.0004396652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004995452,"about_ca_topic_score_gemma":0.000001173979,"domain_scores_codex":[0.9959246,0.00002395837,0.0005902239,0.001926992,0.00115876,0.0003755018],"domain_scores_gemma":[0.9969036,0.0004476017,0.0002540828,0.001672307,0.0005666826,0.0001557406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006154523,0.0003466682,0.001549741,0.008993951,0.000373326,0.0000732416,0.00825533,0.0001923998,0.3421241,0.05511196,0.5048379,0.07807983],"study_design_scores_gemma":[0.0002734171,0.00001805654,0.0007633884,0.0005024955,0.0001269664,0.000008589275,0.001109266,0.01294229,0.4564482,0.2907448,0.2361309,0.0009315882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804573,0.000674498,0.002343906,0.001097523,0.008085572,0.0005128362,0.0001090727,0.0003920736,0.006327201],"genre_scores_gemma":[0.9793621,0.00002198762,0.01638877,0.00005281445,0.0005715501,0.00006643006,0.00002548618,0.00002177513,0.003489039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.268707,"threshold_uncertainty_score":0.9994828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06865604739123872,"score_gpt":0.3732528772430578,"score_spread":0.3045968298518191,"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."}}