{"id":"W3205059008","doi":"10.26434/chemrxiv.9758558.v2","title":"From Desktop to Benchtop – A Paradigm Shift in Asymmetric Synthesis","year":2019,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Workflow; Computer science; Toolbox; Chemical space; Modular design; Paradigm shift; Scope (computer science); Biochemical engineering; Nanotechnology; Chemistry; Drug discovery; Programming language; Engineering; Database","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002000311,0.0005773772,0.001013638,0.0005985732,0.00008169764,0.0005891241,0.002485173,0.0004892356,0.002582685],"category_scores_gemma":[0.002194645,0.0005588361,0.0001567958,0.0007519927,0.0001220524,0.0001734682,0.002066988,0.0006541976,0.005630842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003061803,"about_ca_system_score_gemma":0.0002912167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003872556,"about_ca_topic_score_gemma":0.00009100076,"domain_scores_codex":[0.9953831,0.0003601676,0.0008067826,0.001840167,0.000769866,0.0008398846],"domain_scores_gemma":[0.9964929,0.0008913018,0.0004134678,0.001886048,0.00003891241,0.0002773826],"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":[0.0003735298,0.0007938542,0.07351037,0.001696577,0.00007721324,0.0002932885,0.009426493,0.136978,0.7611876,0.002901408,0.006135004,0.006626703],"study_design_scores_gemma":[0.0006114177,0.0001031349,0.2095311,0.001776089,0.000110942,0.000007218487,0.00009053847,0.007225775,0.727298,0.04570603,0.004734364,0.002805377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863442,0.0002244129,0.004273554,0.001751018,0.003582967,0.0007798555,0.00006347812,0.0002330301,0.002747554],"genre_scores_gemma":[0.9872712,0.0000166906,0.01136124,0.0003277952,0.0004925523,0.0002988635,0.0000263755,0.00006640281,0.0001388233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1360207,"threshold_uncertainty_score":0.9996863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985610762505611,"score_gpt":0.2744068121405985,"score_spread":0.2545507045155423,"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."}}