{"id":"W2033301070","doi":"10.1145/1107499.1107519","title":"Christos Faloutsos speaks out","year":2005,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Very large database; Computer science; Library science; Database; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002158304,0.0001656801,0.0002294666,0.00009609454,0.0001624203,0.0001734098,0.001525331,0.0000671437,0.0002694828],"category_scores_gemma":[0.0001292497,0.0001447559,0.0001618774,0.0002764258,0.00003298362,0.0005487937,0.0005645697,0.0001621403,0.0008717024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004714103,"about_ca_system_score_gemma":0.00003174552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008852494,"about_ca_topic_score_gemma":0.0002501342,"domain_scores_codex":[0.9986048,0.00003346146,0.0003106479,0.0004433507,0.0002324832,0.0003752359],"domain_scores_gemma":[0.9984251,0.00008006499,0.000131854,0.00118035,0.0000610626,0.00012156],"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":[0.000004163061,0.00003847606,0.00109091,0.000004137613,0.00003032607,0.000009129928,0.0007289101,0.0001142805,0.0005369437,0.00195514,0.007238774,0.9882488],"study_design_scores_gemma":[0.0004576549,0.0001791417,0.001636394,0.00002937563,0.00003011822,0.00002719876,0.00008479002,0.1544994,0.002018787,0.003055339,0.8373469,0.0006349387],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214443,0.0008627436,0.7963814,0.01170484,0.001712638,0.0003563991,0.000005758041,0.0009057125,0.06662625],"genre_scores_gemma":[0.8464334,0.00003427576,0.1453943,0.0006015163,0.0006789101,0.000009021901,0.000003285718,0.00001794249,0.006827367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9876139,"threshold_uncertainty_score":0.9999062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02296878735220468,"score_gpt":0.2434831067099527,"score_spread":0.220514319357748,"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."}}