{"id":"W3128787001","doi":"10.2139/ssrn.3390000","title":"Sparse Flexible Design: A Machine Learning Approach","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689234,0.0007554398,0.001500555,0.0009411196,0.0004645244,0.000926798,0.001720336,0.001645979,0.006697329],"category_scores_gemma":[0.004777366,0.0007999502,0.001076428,0.001135547,0.001008685,0.001129976,0.001362652,0.001425223,0.0009982151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003808242,"about_ca_system_score_gemma":0.001060792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008934121,"about_ca_topic_score_gemma":0.001591408,"domain_scores_codex":[0.9992009,0.0003693024,0.00002884594,0.0001156796,0.0002252498,0.00006010016],"domain_scores_gemma":[0.9981071,0.001191285,0.0001450081,0.0002616071,0.0002319843,0.00006292766],"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":[0.0001384331,0.0001346002,0.0003944878,0.000242708,0.00007612089,0.0001040332,0.00005819427,0.7340133,0.003797424,0.1232242,0.003618099,0.1341984],"study_design_scores_gemma":[0.00002454321,0.00006988535,0.00003739313,0.00001291497,0.00001481632,0.00002581515,0.000006344032,0.969686,0.0004585447,0.02832363,0.001332951,0.000007157051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002584341,0.0001127129,0.9952706,0.0001206438,0.0000302425,0.00003755382,0.00002754604,0.000155404,0.001661082],"genre_scores_gemma":[0.2685422,0.0005393809,0.7213001,0.0004392976,0.0001648209,0.0005174008,0.0002774331,0.0002244533,0.007994795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006697329,"threshold_uncertainty_score":0.02240479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390446377519147,"score_gpt":0.2710362509008694,"score_spread":0.2471317871256779,"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."}}