{"id":"W2154632398","doi":"10.1177/1087057110377497","title":"Experimental Design and Statistical Methods for Improved Hit Detection in High-Throughput Screening","year":2010,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre; McGill University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Replicate; Benchmark (surveying); Statistical power; Computer science; Preprocessor; Identification (biology); Statistical inference; Statistical hypothesis testing; Inference; Receiver operating characteristic; Statistics; Column (typography); Type I and type II errors; False positive rate; Word error rate; Data mining; Artificial intelligence; Mathematics; Machine learning","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004289056,0.0002249437,0.0005855173,0.00007633282,0.00009436138,0.0001250545,0.000139957,0.0002378932,0.00007995554],"category_scores_gemma":[0.04760009,0.000199872,0.00007642846,0.0001163091,0.0002395497,0.0002475152,0.0001153093,0.0004242047,0.000002158908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004178456,"about_ca_system_score_gemma":0.00004249225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004846725,"about_ca_topic_score_gemma":0.00002165768,"domain_scores_codex":[0.9973307,0.0009216745,0.0007129128,0.0005251042,0.00013222,0.000377428],"domain_scores_gemma":[0.9407291,0.05866218,0.000164028,0.0002940911,0.00003864763,0.0001119741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001209944,0.0003616494,0.00005579834,0.0001079433,0.00008019048,0.000005504027,0.0001561676,0.0000043343,0.3653893,0.3832362,0.0001883534,0.2492046],"study_design_scores_gemma":[0.001858698,0.0003737382,0.0004463679,0.00002346169,0.0000544348,0.000005074281,0.00007496406,0.01140629,0.1917808,0.7936379,0.0001064679,0.0002317873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02994762,0.00002801849,0.967699,0.00007430698,0.0009935696,0.00106189,0.00008626153,0.00005737812,0.00005193091],"genre_scores_gemma":[0.1296438,0.000003352928,0.8697143,0.00007169086,0.0002249353,0.0002288528,0.000002800512,0.0000474338,0.00006283894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4104017,"threshold_uncertainty_score":0.9604224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3567257914921463,"score_gpt":0.5674295538723138,"score_spread":0.2107037623801674,"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."}}