{"id":"W3165450499","doi":"10.3233/shti210284","title":"Facilitating Study and Item Level Browsing for Clinical and Epidemiological COVID-19 Studies","year":2021,"lang":"en","type":"book-chapter","venue":"Studies in health technology and informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Klaus Tschira Stiftung; Deutsche Forschungsgemeinschaft","keywords":"Metadata; Computer science; World Wide Web; Coronavirus disease 2019 (COVID-19); Information retrieval; Data science; Medicine; Pathology","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":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002770658,0.000359048,0.001331708,0.0002082193,0.0004226766,0.00001051213,0.0001272978,0.0009346206,7.781604e-7],"category_scores_gemma":[0.02029567,0.0002808371,0.0000622123,0.00006114118,0.003266379,0.000005356128,0.000947591,0.0006113775,3.009322e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004273842,"about_ca_system_score_gemma":0.0001692483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003491076,"about_ca_topic_score_gemma":0.0000888681,"domain_scores_codex":[0.9973959,0.0001103114,0.001562234,0.0004534359,0.00009263444,0.0003855198],"domain_scores_gemma":[0.9968814,0.002076481,0.000501268,0.0002682319,0.0001408164,0.0001318451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003489432,0.0001719562,0.09990777,0.01573421,0.002846065,0.00004829839,0.01767403,0.00000412711,0.000004677884,0.03247659,0.01656893,0.8142144],"study_design_scores_gemma":[0.005376483,0.01088079,0.002750555,0.001856128,0.0002008897,0.0003105188,0.3413887,0.0002418314,0.000006929558,0.05093756,0.5847324,0.001317273],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.2850424,0.6959187,0.002712644,0.01123042,0.0009419602,0.002739917,0.0002695803,0.0001771806,0.0009672117],"genre_scores_gemma":[0.1031506,0.7825302,0.09038321,0.01152586,0.0003688008,0.0004277713,0.0001471836,0.00007049942,0.01139585],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8128971,"threshold_uncertainty_score":0.9999644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4433033380863028,"score_gpt":0.5242817043907728,"score_spread":0.08097836630446997,"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."}}