{"id":"W4416622012","doi":"10.5256/f1000research.53939.r79888","title":"Referee report. For: Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches [version 1; peer review: 1 approved with reservations]","year":2021,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Pharmacological Receptor Mechanisms and Effects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Concordia University; Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China; Compute Canada","keywords":"Drug; Biological network; Drug candidate; Drug discovery; Drug repositioning; Precision medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002876827,0.0001560023,0.0002751093,0.00003945757,0.0003956949,0.00005557346,0.0001637484,0.0001641233,0.00001740091],"category_scores_gemma":[0.00285121,0.0001111472,0.00008182361,0.0001191081,0.0001568643,0.00001536961,0.0003397956,0.0002054666,1.767541e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003433456,"about_ca_system_score_gemma":0.0001679473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007282164,"about_ca_topic_score_gemma":0.00004853694,"domain_scores_codex":[0.9980252,0.0002433803,0.0002691127,0.000606054,0.0004570211,0.0003991883],"domain_scores_gemma":[0.9979478,0.0001277811,0.0001022661,0.0002199803,0.001345277,0.0002569056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002982503,0.0001221518,0.0005230103,0.003036826,0.0003597419,0.00005595875,0.0002223235,0.005894616,0.8349046,0.0003984253,0.1471381,0.004361776],"study_design_scores_gemma":[0.008283268,0.002301416,0.0005412374,0.0009916759,0.0002825925,0.0003054844,0.002714786,0.3608195,0.2614665,0.004642846,0.3562643,0.001386386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8406466,0.0106869,0.1280082,0.01801545,0.00009390742,0.001894587,0.000567339,0.00002191806,0.00006502957],"genre_scores_gemma":[0.9763568,0.00309129,0.00920978,0.0003083399,0.0001567693,0.000230701,0.007554531,0.00002150753,0.003070283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5734381,"threshold_uncertainty_score":0.4532453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1131912012559993,"score_gpt":0.396314277349507,"score_spread":0.2831230760935077,"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."}}