{"id":"W2942432801","doi":"10.3390/app9091733","title":"Spatial Data Reconstruction via ADMM and Spatial Spline Regression","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Smoothing; Spatial analysis; Data mining; Aggregate (composite); Spline (mechanical); Regression; Mathematics; Statistics; Computer vision","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001825792,0.0000890973,0.0001467484,0.00009515002,0.000949158,0.0001427763,0.0005117096,0.00007744441,0.001054849],"category_scores_gemma":[0.00008689611,0.00007278063,0.00002152654,0.0003966672,0.0009625881,0.0002850794,0.0001207597,0.00009763397,0.000122344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002959382,"about_ca_system_score_gemma":0.0002129394,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02172041,"about_ca_topic_score_gemma":0.05176588,"domain_scores_codex":[0.9984763,0.00009245364,0.0001937168,0.0005405878,0.0004714699,0.0002254955],"domain_scores_gemma":[0.9992439,0.0001525428,0.0001164767,0.0003499163,0.00004160672,0.00009554873],"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":[0.00001951688,0.00003781922,0.02573123,0.00001149227,0.000009439706,3.233814e-7,0.001854757,0.00005480663,0.002323218,0.003781928,0.0001410871,0.9660344],"study_design_scores_gemma":[0.004536054,0.0008609761,0.1123448,0.0004665619,0.0005028751,0.00002079578,0.09252213,0.5733287,0.01144405,0.07916665,0.1209416,0.003864834],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949943,0.00008561959,0.01630028,0.00152884,0.0005162484,0.0004266071,0.00001456669,0.00007215642,0.03111267],"genre_scores_gemma":[0.9986727,0.00003441617,0.000580669,0.00009924696,0.0002995222,0.000006249457,0.00002865182,0.000003157491,0.0002753609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9621695,"threshold_uncertainty_score":0.9998583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283433055003318,"score_gpt":0.3126979958033424,"score_spread":0.2798636652533092,"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."}}