{"id":"W3213179908","doi":"10.1109/bigdata52589.2021.9671411","title":"Detecting Fake Points of Interest from Location Data","year":2021,"lang":"en","type":"preprint","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Point of interest; Data mining; Ground truth; Reliability (semiconductor); Global Positioning System; Artificial intelligence; Perceptron; Machine learning; Artificial neural network","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":[0.001218966,0.0008585652,0.0009558598,0.002604105,0.0005514409,0.001109929,0.001199153,0.001541866,0.0008161927],"category_scores_gemma":[0.009329661,0.0002948638,0.0005490303,0.002009642,0.000614125,0.002135688,0.001268819,0.001261949,0.001026679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008543444,"about_ca_system_score_gemma":0.0004742933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002646475,"about_ca_topic_score_gemma":0.002882036,"domain_scores_codex":[0.9981933,0.0003970059,0.0001177833,0.0004274982,0.0006487216,0.000215575],"domain_scores_gemma":[0.9943956,0.001957088,0.00125109,0.001433493,0.0008137083,0.0001490157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001154813,0.0004577001,0.1288467,0.0006365275,0.0002795262,0.001556669,0.0005953696,0.1011102,0.02100273,0.007698298,0.0145201,0.7221414],"study_design_scores_gemma":[0.00002247456,0.0001589691,0.03317219,0.00006897598,0.00005311782,0.001109022,0.0003808147,0.9303055,0.02193828,0.006436053,0.006310991,0.00004353065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6130143,0.002075823,0.3722289,0.001385355,0.0003776138,0.0001472283,0.002690449,0.003547769,0.004532573],"genre_scores_gemma":[0.9487462,0.0004470109,0.04669742,0.00006878152,0.00009848936,0.00003656347,0.002242735,0.00004015068,0.001622616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002646475,"threshold_uncertainty_score":0.00644654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5344346287500138,"score_gpt":0.3894898064020511,"score_spread":0.1449448223479627,"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."}}