{"id":"W3154676607","doi":"10.12688/wellcomeopenres.16718.1","title":"An automated approach to identify scientific publications reporting pharmacokinetic parameters","year":2021,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre","funders":"Medical Research Council; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; Alan Turing Institute; University College London; Great Ormond Street Hospital for Children; Wellcome Trust","keywords":"Computer science; Pipeline (software); Pooling; Information retrieval; Set (abstract data type); Data mining; Machine learning; Data science; Artificial intelligence","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","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.03205878,0.0004200736,0.0007198127,0.002100436,0.001185362,0.03655852,0.01645886,0.000236497,0.0001103133],"category_scores_gemma":[0.004182271,0.0004561888,0.0002173533,0.006803588,0.0002529838,0.002123142,0.02750905,0.002017155,0.0002710531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005209715,"about_ca_system_score_gemma":0.003371896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054345,"about_ca_topic_score_gemma":0.00001979822,"domain_scores_codex":[0.9854543,0.004395078,0.001883633,0.003818184,0.003210936,0.00123783],"domain_scores_gemma":[0.9890883,0.001135868,0.0009198319,0.005314627,0.002673686,0.0008676895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002596895,0.002154964,0.0007267577,0.0004909622,0.0002485381,0.0001384044,0.004721594,0.8735704,0.01069322,0.02349013,0.04713241,0.03660668],"study_design_scores_gemma":[0.0001919635,0.00003816504,0.009427305,0.000183894,0.0000127271,0.00004265353,0.0002452308,0.976582,0.002533581,0.006951591,0.003233267,0.0005576506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3526258,0.0002395538,0.5985812,0.005317634,0.002559854,0.005790184,0.00006132893,0.001243468,0.03358097],"genre_scores_gemma":[0.4623114,0.000008962107,0.5337024,0.0001463851,0.00008656173,0.001343088,0.0007373875,0.00005821236,0.00160566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1096856,"threshold_uncertainty_score":0.999789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.417953394708161,"score_gpt":0.5600628459605473,"score_spread":0.1421094512523863,"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."}}