{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001761654,0.00008527188,0.0001648272,0.0001195591,0.0002515735,0.0001242792,0.0007417498,0.00003248722,0.0003342109],"category_scores_gemma":[0.0003972046,0.00005950234,0.00002316318,0.0006055881,0.00009265981,0.0002552255,0.0002155149,0.00009571779,0.003181244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003290173,"about_ca_system_score_gemma":0.00007467208,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02859393,"about_ca_topic_score_gemma":0.07966313,"domain_scores_codex":[0.9984961,0.0001896031,0.0003082906,0.0002038743,0.0005111734,0.0002910114],"domain_scores_gemma":[0.9986541,0.0004335257,0.00008967119,0.0007133773,0.00006788023,0.00004148711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003214701,0.00008521893,0.4823952,0.00007619554,0.0001217596,0.000004304476,0.4119129,0.0001191207,0.00002321313,0.01810019,0.06534177,0.02178803],"study_design_scores_gemma":[0.000351354,0.00001439087,0.1334209,0.0001333631,0.00001905303,4.185684e-7,0.05324259,0.0008073025,0.00000267512,0.001878773,0.809899,0.0002302392],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7862403,0.00007246775,0.0001402437,0.01422598,0.0009370495,0.001850293,0.0000851127,0.00007703067,0.1963716],"genre_scores_gemma":[0.9974837,0.000005355097,0.0003243636,0.0008695871,0.0001573726,0.00004179722,0.00003088381,0.000004135773,0.001082747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7445572,"threshold_uncertainty_score":0.9975949,"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."}}