{"id":"W1973068771","doi":"10.5194/isprsannals-i-4-321-2012","title":"CONFLATION OF NATIONAL BRIDGE INVENTORY DATABASE WITH TIGERBASED ROAD VECTORS","year":2012,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geoscience BC","funders":"","keywords":"Conflation; Bridge (graph theory); Geospatial analysis; Database; Computer science; Similarity (geometry); Matching (statistics); Information retrieval; Data mining; Geography; Artificial intelligence; Cartography; Mathematics","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":[0.002273682,0.0003651266,0.0005685954,0.005404393,0.0005226439,0.001923501,0.001583693,0.0005448122,0.004089466],"category_scores_gemma":[0.01005357,0.000338271,0.0004953853,0.00478171,0.0002991388,0.002422327,0.002207946,0.0004939862,0.002040965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00071299,"about_ca_system_score_gemma":0.001032694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005333073,"about_ca_topic_score_gemma":0.006289535,"domain_scores_codex":[0.9970927,0.00053506,0.0003941211,0.0005110544,0.001308221,0.0001587707],"domain_scores_gemma":[0.994138,0.001029584,0.0005871315,0.00242813,0.001683457,0.0001337233],"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":[0.0008121908,0.0005083942,0.04746848,0.0005116857,0.0001658772,0.0009439853,0.00159499,0.02552293,0.04351683,0.01720857,0.04172129,0.8200248],"study_design_scores_gemma":[0.000106347,0.0005863421,0.04932364,0.000176942,0.0001316451,0.001376457,0.002512652,0.590932,0.1910101,0.008833114,0.1548364,0.0001744671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.265573,0.0003912507,0.6686722,0.0004912046,0.0001966546,0.001027647,0.01986119,0.02898014,0.01480664],"genre_scores_gemma":[0.4772424,0.0002133097,0.4775932,0.0001443551,0.00002700363,0.0004703182,0.0400971,0.0006429848,0.003569209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005404393,"threshold_uncertainty_score":0.01368064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09622989958546337,"score_gpt":0.3472863436046432,"score_spread":0.2510564440191798,"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."}}