{"id":"W4304478658","doi":"10.1109/jbhi.2022.3212863","title":"Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Anhui Provincial Key Research and Development Plan; Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China; Nanyang Technological University; Prime Minister's Office Singapore; National Research Foundation","keywords":"Computer science; Granularity; Weighting; Data mining; Feature (linguistics); Machine learning; Artificial intelligence; Data science","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":[],"consensus_categories":[],"category_scores_codex":[0.003701633,0.00009371599,0.0002573631,0.0001892998,0.0002732409,0.00002737753,0.0002185125,0.0001112248,0.000006422797],"category_scores_gemma":[0.0001052363,0.00007396077,0.00009811054,0.000157127,0.0001692997,0.00002131584,0.0001180721,0.0003709905,2.139852e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001068848,"about_ca_system_score_gemma":0.00187331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001891459,"about_ca_topic_score_gemma":0.000006068983,"domain_scores_codex":[0.9979701,0.00005633282,0.00117921,0.00006184071,0.0004379033,0.0002946111],"domain_scores_gemma":[0.9985142,0.0000563156,0.0007268041,0.0001383138,0.00008238009,0.0004819943],"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":[0.00230072,0.0005578289,0.000725732,0.005459581,0.0005598114,0.000003865481,0.0173484,0.006015257,0.002805206,0.001142036,0.5741084,0.3889732],"study_design_scores_gemma":[0.006281693,0.01560829,0.0006435427,0.0001380297,0.00005758476,0.0007637234,0.01159927,0.08868533,0.000349493,0.006780776,0.8687667,0.0003255856],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2531607,0.005061146,0.7140012,0.02419916,0.001550382,0.001081971,0.0007801849,0.000008740354,0.0001564165],"genre_scores_gemma":[0.9710099,0.003572519,0.02031317,0.003262502,0.00110018,0.00001894218,0.0003238529,0.00002195959,0.000377021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7178491,"threshold_uncertainty_score":0.3323171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1121773847233858,"score_gpt":0.3830713489773672,"score_spread":0.2708939642539814,"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."}}