{"id":"W4416606839","doi":"10.5256/f1000research.55646.r83072","title":"Referee report. For: Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches [version 2; peer review: 3 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":[0.002872668,0.0001559931,0.0002751675,0.00003945377,0.0003956786,0.00005567423,0.0001635805,0.000164149,0.00001707294],"category_scores_gemma":[0.002841777,0.000111131,0.00008182636,0.0001190815,0.0001568751,0.00001538118,0.0003394219,0.0002054747,1.78252e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003444437,"about_ca_system_score_gemma":0.0001679571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007336415,"about_ca_topic_score_gemma":0.00004862756,"domain_scores_codex":[0.998026,0.0002430899,0.0002692585,0.0006058812,0.0004565441,0.000399189],"domain_scores_gemma":[0.9979456,0.0001269473,0.0001022845,0.0002196738,0.00134856,0.0002569292],"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":[0.003013259,0.0001226687,0.0005271693,0.003094441,0.0003611993,0.00005674871,0.000223353,0.0059229,0.8404501,0.0003983419,0.1413948,0.004435038],"study_design_scores_gemma":[0.008310844,0.00228082,0.000538396,0.0009824679,0.0002783494,0.0003025623,0.002683878,0.3584423,0.2636933,0.004616198,0.3564957,0.00137523],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8413009,0.01066868,0.1277231,0.01768494,0.00009395179,0.001884808,0.0005568919,0.00002175435,0.0000649713],"genre_scores_gemma":[0.9765818,0.003114514,0.009091755,0.0003068646,0.0001564691,0.00022664,0.007425823,0.00002142108,0.003074738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5767568,"threshold_uncertainty_score":0.453179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.11183550962596,"score_gpt":0.3959307160202047,"score_spread":0.2840952063942447,"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."}}