{"id":"W4245842275","doi":"10.26434/chemrxiv-2021-k0qx5","title":"Routescore: Punching the Ticket to More Efficient Materials Development","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":1,"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; University of Toronto; Government of Ontario","keywords":"Bottleneck; Computer science; Punching; Chemical space; Throughput; Generality; Space (punctuation); Embedded system; Engineering; Drug discovery; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003212823,0.001351841,0.0008941576,0.0009659859,0.0006082942,0.002326553,0.001828003,0.001607788,0.01349317],"category_scores_gemma":[0.004746042,0.0007045953,0.0008498127,0.0006023076,0.001281084,0.002771477,0.00225826,0.003029001,0.004238086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001322226,"about_ca_system_score_gemma":0.002626224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007421601,"about_ca_topic_score_gemma":0.001489385,"domain_scores_codex":[0.9984211,0.0004593608,0.00007076056,0.0002457217,0.0006516861,0.0001514034],"domain_scores_gemma":[0.9981402,0.0007083312,0.0002025557,0.0005511903,0.0002619743,0.0001357019],"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":[0.001600991,0.0008869379,0.001846498,0.001601654,0.0001807874,0.0003460371,0.000306462,0.1510634,0.3269193,0.1616604,0.04597048,0.3076169],"study_design_scores_gemma":[0.0003511778,0.00133193,0.0007691852,0.000157236,0.00006337665,0.0003227696,0.0001030575,0.3168183,0.4588349,0.05441737,0.166645,0.0001856563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1075087,0.002550974,0.8010044,0.002914544,0.0009254143,0.0006679139,0.002362261,0.03689139,0.04517443],"genre_scores_gemma":[0.1884233,0.001576248,0.7920581,0.0006750604,0.00008908388,0.001059583,0.001935013,0.005440587,0.008743086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01349317,"threshold_uncertainty_score":0.04513919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754084304222621,"score_gpt":0.2448783388310347,"score_spread":0.2273374957888085,"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."}}