{"id":"W4414438897","doi":"10.26434/chemrxiv-2025-zd9mr-v4","title":"A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data","year":2025,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada); Ontario Institute for Cancer Research","funders":"","keywords":"Context (archaeology); Drug discovery; Key (lock); General partnership; Computational model; Coronavirus disease 2019 (COVID-19); Coronavirus; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Best practice","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"],"consensus_categories":[],"category_scores_codex":[0.0008646237,0.0003743876,0.0003647054,0.0001107156,0.0002091593,0.0001512118,0.002323515,0.0005472279,0.00002422741],"category_scores_gemma":[0.0004531218,0.0003442593,0.0001593516,0.00008256837,0.0003819933,0.000007631645,0.006451497,0.001311281,0.00004904436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004780134,"about_ca_system_score_gemma":0.000696992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001399062,"about_ca_topic_score_gemma":0.0001441833,"domain_scores_codex":[0.9977382,0.00019878,0.0004850839,0.0006181572,0.0005455193,0.0004142109],"domain_scores_gemma":[0.996919,0.0001392028,0.0001737645,0.002414566,0.0001799127,0.0001735599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00523694,0.01705022,0.002778288,0.01737766,0.005029768,0.00009543409,0.005025658,0.006268253,0.0220047,0.002161177,0.5497661,0.3672058],"study_design_scores_gemma":[0.01441629,0.003074383,0.01271441,0.003016723,0.0006730671,0.00002970878,0.003384352,0.03651784,0.1358633,0.0301355,0.7535865,0.006587888],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9571257,0.003056166,0.005234797,0.002925405,0.00157563,0.001319743,0.002350461,0.00007029938,0.02634181],"genre_scores_gemma":[0.9714904,0.002032825,0.001467493,0.000693744,0.0005500673,0.00004714915,0.01955398,0.00002693719,0.00413745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3606179,"threshold_uncertainty_score":0.9999009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029940180897501,"score_gpt":0.3681273187338105,"score_spread":0.2651333006440604,"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."}}