{"id":"W4399039386","doi":"10.1109/access.2024.3405529","title":"Fully Automated Scholarly Search for Biomedical Systematic Literature Reviews","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Council Canada","keywords":"Computer science; Information retrieval; Benchmark (surveying); Suite; Set (abstract data type); Generative grammar; Precision and recall; Process (computing); Recall; Field (mathematics); Data mining; Data science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008769811,0.0001723855,0.0003078429,0.0001092426,0.00008255361,0.0007566907,0.0005698769,0.0003641906,0.000011518],"category_scores_gemma":[0.0004832371,0.0001158246,0.0001651126,0.000375474,0.0001150572,0.00002135392,0.00009893711,0.0002083641,0.00004932684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001619536,"about_ca_system_score_gemma":0.0001037059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003316362,"about_ca_topic_score_gemma":0.000002058373,"domain_scores_codex":[0.998572,0.0001393125,0.0003581761,0.0004259441,0.0002000985,0.0003044788],"domain_scores_gemma":[0.9992878,0.00007517457,0.00004332947,0.00034871,0.0001143365,0.0001306506],"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":[0.0001272145,0.0001849545,0.0001826413,0.1215315,0.0004798203,0.0001326793,0.0005019344,0.000004931209,0.3002703,0.0003441294,0.533287,0.04295288],"study_design_scores_gemma":[0.001310436,0.001247407,0.0003234472,0.03633209,0.000293731,0.0003311809,0.0001129019,0.00947073,0.08236105,0.0003412934,0.8667998,0.00107592],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2700672,0.5800685,0.1254036,0.006582618,0.008930356,0.005188225,0.000513066,0.002041187,0.001205156],"genre_scores_gemma":[0.9828174,0.004137862,0.004993122,0.001341382,0.001992934,0.000766477,0.0005785343,0.0000802228,0.00329205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7127502,"threshold_uncertainty_score":0.7296786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05230406408299433,"score_gpt":0.3899084845378799,"score_spread":0.3376044204548856,"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."}}