{"id":"W4244413936","doi":"10.26434/chemrxiv.13008500.v2","title":"Automatic discovery of chemical reactions using imposed activation","year":2020,"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":"Vector Institute; Canadian Institute for Advanced Research; University of Toronto","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Advanced Research Projects Agency; Defense Advanced Research Projects Agency","keywords":"Reactivity (psychology); Chemical reaction; Drug discovery; Chemistry; Natural product; Biochemical engineering; Decomposition; Computer science; Combinatorial chemistry; Computational chemistry; Molecule; Quantum chemical; Biological system; Organic chemistry; Engineering","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":[0.0005656412,0.0002878548,0.0005320648,0.00008864616,0.00007135863,0.000231947,0.0007329039,0.0002484804,0.0005737114],"category_scores_gemma":[0.001022025,0.0002764104,0.0001391961,0.0002121165,0.0002366994,0.0003417828,0.0008746864,0.0004247154,0.00005702087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001616129,"about_ca_system_score_gemma":0.0003042961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002107704,"about_ca_topic_score_gemma":2.873628e-7,"domain_scores_codex":[0.997737,0.0001320902,0.000680553,0.0006776421,0.0004921487,0.0002805521],"domain_scores_gemma":[0.9980989,0.0001545589,0.0008839561,0.0006752572,0.00009854677,0.00008881449],"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":[0.00001422861,0.0000430737,0.0001097735,0.0007129824,0.000008199714,0.000001617376,0.0002140128,0.001980101,0.9966367,0.0001609383,0.00006858526,0.00004977626],"study_design_scores_gemma":[0.0001139623,0.00001217995,0.000442501,0.0003593443,0.00003491577,0.000004846097,0.00002129257,0.0717591,0.925092,0.001887721,0.00002522905,0.0002468837],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897426,0.00001162919,0.008163282,0.0003542253,0.0008995715,0.0002886751,0.00001273804,0.0002242864,0.0003030106],"genre_scores_gemma":[0.9682568,0.000002407473,0.0312941,0.00005391851,0.0002690231,0.00002192618,0.00003503272,0.00003618805,0.00003062059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07154469,"threshold_uncertainty_score":0.9999688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03726095429326588,"score_gpt":0.3033747144374899,"score_spread":0.266113760144224,"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."}}