{"id":"W2046470653","doi":"10.1109/itaic.2014.7065000","title":"Using data mining techniques to improve location based services","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Data mining; Process (computing); Location-based service; Service (business); Location data; Quality (philosophy); Data deduplication; Database","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":[0.0033085,0.001647752,0.001589878,0.007908354,0.0009972351,0.002704343,0.0020048,0.00132678,0.001007464],"category_scores_gemma":[0.01457997,0.0006718673,0.001721446,0.007941881,0.0004154514,0.003310562,0.001465368,0.001573016,0.001191708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008129125,"about_ca_system_score_gemma":0.00154259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005632578,"about_ca_topic_score_gemma":0.006159042,"domain_scores_codex":[0.9968531,0.0006456241,0.0005678133,0.0005228578,0.001263558,0.0001470982],"domain_scores_gemma":[0.9926659,0.00312887,0.0008162246,0.001145634,0.002108229,0.0001350128],"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":[0.0002924392,0.0008344589,0.03710685,0.0009006,0.0005245106,0.0007283203,0.0005902298,0.08148947,0.01246256,0.005887645,0.006269003,0.8529139],"study_design_scores_gemma":[0.00005762813,0.0002380691,0.007750435,0.0002252189,0.0002637614,0.0006515675,0.0008082953,0.9210724,0.02729428,0.01918836,0.02237061,0.00007937701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05761187,0.002681987,0.9272109,0.001831295,0.0001986172,0.0003979391,0.002364107,0.004804621,0.002898613],"genre_scores_gemma":[0.2487582,0.001978063,0.7438737,0.000231357,0.00009215831,0.0002557807,0.003711027,0.0001213153,0.0009783686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007908354,"threshold_uncertainty_score":0.01749724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04931969447216452,"score_gpt":0.3037207621028979,"score_spread":0.2544010676307334,"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."}}