{"id":"W2890739803","doi":"10.1109/tpami.2017.2757489","title":"Ghost Numbers","year":2017,"lang":"en","type":"letter","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Partition (number theory); Set (abstract data type); Image (mathematics); Pattern recognition (psychology); Machine learning; Algorithm; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001725905,0.0004346406,0.0005475958,0.000736232,0.0006690149,0.0005304408,0.001522231,0.0004274443,0.0002612691],"category_scores_gemma":[0.000003788121,0.0003994376,0.0005946259,0.000631617,0.0001460649,0.000247924,0.00001433487,0.001483186,0.0001417421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005698631,"about_ca_system_score_gemma":0.00003453255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002269486,"about_ca_topic_score_gemma":0.0004041312,"domain_scores_codex":[0.9977072,0.00007461133,0.0004619547,0.001016137,0.0003880925,0.000351969],"domain_scores_gemma":[0.9973938,0.0001523727,0.0003484497,0.001890508,0.0001008276,0.0001140589],"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":[0.000002419388,0.00008567098,0.00003549092,0.00004311914,0.0009953442,0.00009351612,0.0001184086,0.000397333,0.00001684542,0.0001485884,0.02176237,0.9763009],"study_design_scores_gemma":[0.0002184561,0.0005866142,0.0003248422,0.000295847,0.004713707,0.0002850599,0.00003646651,0.1449543,0.136808,0.008131136,0.6997713,0.003874261],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000006343716,0.00005812357,0.9044184,0.0941482,0.0002561696,0.0001900564,0.0001242289,0.00026087,0.0005375881],"genre_scores_gemma":[0.7702371,0.001502582,0.00949668,0.2080635,0.0004961647,0.0003306405,0.00005830811,0.00007474212,0.0097403],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9724267,"threshold_uncertainty_score":0.9998457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02138022708403561,"score_gpt":0.2796728577036818,"score_spread":0.2582926306196462,"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."}}