{"id":"W2167855612","doi":"10.5194/isprsarchives-xl-2-w1-149-2013","title":"ASSESSING VOLUNTEERED GEOGRAPHIC INFORMATION (VGI) QUALITY BASED ON CONTRIBUTORS' MAPPING BEHAVIOURS","year":2013,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre de Géomatique du Québec; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Volunteered geographic information; Data science; Citizen science; Computer science; Quality (philosophy); Information retrieval; Data quality; Geography; Data mining; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01671432,0.0003282208,0.0003618569,0.003529392,0.001106917,0.002863518,0.0006740161,0.000478967,0.002059437],"category_scores_gemma":[0.09308235,0.0001917899,0.000284052,0.002416229,0.0008888397,0.002035417,0.002222472,0.0004942738,0.0004895663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007577376,"about_ca_system_score_gemma":0.0006933318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003768695,"about_ca_topic_score_gemma":0.004144854,"domain_scores_codex":[0.989589,0.006877033,0.0006041362,0.0007157593,0.001890534,0.0003236358],"domain_scores_gemma":[0.8808928,0.07891494,0.01055139,0.009077118,0.01756931,0.002994434],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003735989,0.0001753508,0.8370686,0.0003307681,0.0001124769,0.0002185812,0.07483368,0.002231564,0.002171948,0.001660104,0.001862298,0.0789611],"study_design_scores_gemma":[0.00005809014,0.0005617712,0.8167685,0.0004716468,0.000161334,0.000376266,0.1264798,0.02460466,0.003615657,0.005243899,0.02150826,0.0001500918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890441,0.00007230191,0.004958407,0.0002375899,0.00002168774,0.0001297925,0.0002577376,0.00004720217,0.005231149],"genre_scores_gemma":[0.9945998,0.00005393258,0.004052678,0.00001785671,0.000009209071,0.00008553224,0.0002051796,0.00001497451,0.0009608284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9832857,"threshold_uncertainty_score":0.08839482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02720895091124341,"score_gpt":0.2928486587973426,"score_spread":0.2656397078860991,"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."}}