{"id":"W2001515828","doi":"10.1021/ci050367t","title":"Collaborative Filtering on a Family of Biological Targets","year":2006,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada); Université de Montréal","funders":"AstraZeneca","keywords":"Computer science; Collaborative filtering; Machine learning; Artificial intelligence; Generalization; Prioritization; Artificial neural network; Task (project management); Representation (politics); Kernel (algebra); Data mining; Recommender system","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":[],"consensus_categories":[],"category_scores_codex":[0.00009643253,0.00004206868,0.00009286216,0.00004248336,0.00002164984,0.00003584777,0.000110378,0.00002951556,7.100235e-7],"category_scores_gemma":[0.00001201385,0.00002956167,0.00002751049,0.0001029207,0.00001312435,0.0004497991,0.00003111664,0.00007707786,6.30164e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007987111,"about_ca_system_score_gemma":0.00001553102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001485421,"about_ca_topic_score_gemma":2.209016e-8,"domain_scores_codex":[0.9994622,0.000005721994,0.0003438554,0.00003158222,0.0000988395,0.0000578111],"domain_scores_gemma":[0.9995511,0.00002751842,0.0001882615,0.00004548496,0.0001566595,0.00003096148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009936185,0.0001316764,0.00004439808,0.00003341033,0.00002242923,0.000002154897,0.0008038305,0.2425425,0.594358,0.1026043,0.00257385,0.05678414],"study_design_scores_gemma":[0.0002825747,0.00006572201,0.00002815276,0.00003647241,0.000001977845,0.00001236782,0.00004381958,0.9358198,0.05762888,0.004596426,0.001421959,0.00006185794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5233039,0.00006408332,0.475873,0.0002562879,0.00002383497,0.00002574034,0.0000010591,0.000005529123,0.0004466521],"genre_scores_gemma":[0.9786968,0.00004349034,0.0209979,0.0002194844,0.00003844686,9.119455e-7,0.000001330565,7.066466e-7,8.97611e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6932773,"threshold_uncertainty_score":0.120549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02375739911611089,"score_gpt":0.2549755280878101,"score_spread":0.2312181289716992,"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."}}