{"id":"W3205219207","doi":"10.26434/chemrxiv.12286877.v1","title":"Coronavirus canSAR – a Data-Driven, AI-Enabled, Drug Discovery Resource for the Research Community","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Cancer Research UK; Wellcome Trust","keywords":"Drug discovery; Coronavirus disease 2019 (COVID-19); Data science; Resource (disambiguation); Coronavirus; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Medicine; Infectious disease (medical specialty); Bioinformatics; Biology; Disease","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","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.008255087,0.0004929043,0.0006224156,0.0001829019,0.001377649,0.002004648,0.01743116,0.0002266307,0.000009100936],"category_scores_gemma":[0.00217421,0.0004030007,0.0002782628,0.0008780929,0.0005356632,0.0009492612,0.03057118,0.004650219,0.00004459429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003605512,"about_ca_system_score_gemma":0.001986491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002027161,"about_ca_topic_score_gemma":0.0003289361,"domain_scores_codex":[0.9928355,0.00281926,0.0006341283,0.001550988,0.001359571,0.0008005247],"domain_scores_gemma":[0.979448,0.01226477,0.0002997154,0.007229596,0.0005202774,0.0002375945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004361178,0.0008564277,0.0004093278,0.002238755,0.0008337051,0.00008877684,0.01447211,0.2787742,0.0006686267,0.1034731,0.5590479,0.03870096],"study_design_scores_gemma":[0.0004936211,0.00004775709,0.00107584,0.0002089085,0.00006004182,0.000009735947,0.0004438756,0.7345605,0.0009444778,0.09240288,0.1691743,0.0005781106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0183559,0.001564462,0.9419324,0.03253778,0.001251074,0.00225263,0.0004051636,0.0003280875,0.001372478],"genre_scores_gemma":[0.8604745,0.0002765474,0.1238389,0.00633724,0.002338962,0.00135224,0.002384606,0.0002396643,0.002757341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8421186,"threshold_uncertainty_score":0.9999224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.370226153391915,"score_gpt":0.4548204876807863,"score_spread":0.08459433428887131,"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."}}