{"id":"W4389703979","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-619-2023","title":"LOW-COST CLOUD-BASED HD-MAP UPDATES FOR INFRASTRUCTURE MANAGEMENT AND MAINTENANCE","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Upload; Map matching; Cloud computing; Cloud database; GNSS applications; Android (operating system); Real-time computing; Data mining; Global Map; Pipeline (software); Database; Matching (statistics); Process (computing); Artificial intelligence; Global Positioning System; Operating system; Robot","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008908127,0.0003717567,0.0003475465,0.0009838702,0.001014005,0.0005940026,0.001214432,0.00009602766,0.000004220304],"category_scores_gemma":[0.0003816189,0.0002413071,0.0002449938,0.0009664247,0.002185576,0.0003399094,0.0006218486,0.0003051716,0.000002892587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004300999,"about_ca_system_score_gemma":0.00008706289,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.167648,"about_ca_topic_score_gemma":0.04019551,"domain_scores_codex":[0.9969391,0.00009503398,0.001049381,0.0003166876,0.001095371,0.0005044497],"domain_scores_gemma":[0.9979066,0.0007003306,0.0007067963,0.0003742513,0.0002090001,0.0001030076],"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":[0.00008529521,0.000006489933,0.0001641639,0.0001247224,0.00007576176,2.691209e-7,0.001234891,0.02986099,0.0004980003,0.00003097931,0.0003503501,0.9675681],"study_design_scores_gemma":[0.0008013234,0.00006530956,0.002362102,0.0003494108,0.00003458278,0.00003147665,0.00196952,0.9625546,0.01081218,0.009650442,0.01109432,0.0002747689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006503364,0.00002450038,0.9838545,0.002716057,0.002048127,0.0008793173,0.000177711,0.0001572726,0.003639104],"genre_scores_gemma":[0.994509,0.0002847142,0.004169235,0.0007482583,0.0001032503,0.000001288478,0.00007958499,0.00001475518,0.00008984537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9880057,"threshold_uncertainty_score":0.9840217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033055749355569,"score_gpt":0.2333487170201138,"score_spread":0.2230181595265581,"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."}}