{"id":"W2774363908","doi":"10.1109/icdm.2017.122","title":"Recover Fine-Grained Spatial Data from Coarse Aggregation","year":2017,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Smoothing; Aggregate (composite); Spatial analysis; Exploit; Spatial distribution; Mobile phone; Sparse matrix; Distribution (mathematics); Spatial contextual awareness","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007125345,0.0000588927,0.0001007412,0.00002699161,0.001364006,0.000322718,0.0008778874,0.0000691191,0.008673416],"category_scores_gemma":[0.001607102,0.0000549727,0.00004130809,0.00004612745,0.0002403181,0.0005103434,0.0001042694,0.00006064046,0.0004456649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003988468,"about_ca_system_score_gemma":0.0001748577,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4425241,"about_ca_topic_score_gemma":0.8148243,"domain_scores_codex":[0.9990683,0.0001191258,0.000142625,0.0002793474,0.0002591934,0.0001314519],"domain_scores_gemma":[0.9983597,0.000158397,0.0001256246,0.001202899,0.00008064372,0.00007267734],"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":[0.00005639899,0.0002539126,0.08479933,0.000008174638,0.0001507896,0.000006275669,0.007782519,0.00006193749,0.0001523928,0.01118002,0.05149264,0.8440556],"study_design_scores_gemma":[0.001732228,0.00006145286,0.1623759,0.00007479203,0.0003346843,9.465143e-8,0.004588361,0.1531477,0.0005974927,0.04433021,0.6318662,0.0008908806],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.602791,0.00008822871,0.08152175,0.0386982,0.001351094,0.000709345,0.0005817696,0.0003267843,0.2739319],"genre_scores_gemma":[0.9904583,0.00001738282,0.0004597246,0.0001725364,0.0005492144,0.00000411161,0.0004395139,0.000003808721,0.007895476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8431647,"threshold_uncertainty_score":0.9999361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1038759998803866,"score_gpt":0.3748337031555117,"score_spread":0.270957703275125,"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."}}