{"id":"W2185052039","doi":"","title":"SPATIAL DATA UNCERTAINTY IN THE VGI WORLD: GOING FROM CONSUMER TO PRODUCER","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Volunteered geographic information; Geography; Spatial analysis; Cartography; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.02346696,0.0005271499,0.0009281138,0.006383758,0.001893265,0.02125181,0.001787292,0.003441368,0.005400967],"category_scores_gemma":[0.09644311,0.0008289855,0.0005984409,0.01476318,0.009801534,0.02664166,0.004710144,0.004132749,0.0009537321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01152086,"about_ca_system_score_gemma":0.003505788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03572732,"about_ca_topic_score_gemma":0.02331134,"domain_scores_codex":[0.9827153,0.005265789,0.000925248,0.002117399,0.00858581,0.000390417],"domain_scores_gemma":[0.909124,0.05645628,0.006601168,0.006146684,0.02058598,0.001085832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002222662,0.00005177629,0.07608473,0.001742146,0.0001767849,0.0005954611,0.02832423,0.005975828,0.00206792,0.2798558,0.09279922,0.5121039],"study_design_scores_gemma":[0.00001515897,0.0000803301,0.04258899,0.00296476,0.0001170969,0.00101194,0.03703604,0.01050563,0.003846448,0.2931259,0.608435,0.0002727805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1109314,0.1003928,0.1446917,0.5451012,0.002647205,0.0001316681,0.005937385,0.001203316,0.08896336],"genre_scores_gemma":[0.8248507,0.07164856,0.06269319,0.02583844,0.003723772,0.00009109321,0.002862256,0.001639608,0.006652399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03572732,"threshold_uncertainty_score":0.1241066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0421253478080758,"score_gpt":0.3194284407909464,"score_spread":0.2773030929828706,"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."}}