{"id":"W2165445075","doi":"10.5194/isprsarchives-xl-2-w1-143-2013","title":"IMPROVING VOLUNTEERED GEOGRAPHIC DATA QUALITY USING SEMANTIC SIMILARITY MEASUREMENTS","year":2013,"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":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Volunteered geographic information; Computer science; Semantic similarity; Information retrieval; Data quality; Semantics (computer science); Profiling (computer programming); Context (archaeology); Linked data; Data mining; Completeness (order theory); Process (computing); Data science; Semantic Web; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.01050207,0.000493029,0.0009857101,0.005955448,0.0007946899,0.003915538,0.001429442,0.0007458772,0.001372617],"category_scores_gemma":[0.04557769,0.0002934699,0.0006433247,0.005622518,0.0009662262,0.005189993,0.003877455,0.0007414949,0.0004530521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182717,"about_ca_system_score_gemma":0.0008832508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003917085,"about_ca_topic_score_gemma":0.002944918,"domain_scores_codex":[0.9900432,0.003763026,0.0007669357,0.001041757,0.00414512,0.0002401116],"domain_scores_gemma":[0.9625427,0.01276731,0.003140235,0.009522635,0.01122824,0.0007988655],"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":[0.001463944,0.0005415445,0.1006343,0.001605148,0.0006191207,0.0008167793,0.008724651,0.07978092,0.02667421,0.03188191,0.01371595,0.7335415],"study_design_scores_gemma":[0.0001402349,0.0005109771,0.05854512,0.0003866692,0.0002598068,0.0009711069,0.008090547,0.7340077,0.06513627,0.07795384,0.05371661,0.000281164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.362104,0.0005842839,0.6204141,0.0007479208,0.0001870123,0.0003338579,0.002760095,0.004627767,0.008241048],"genre_scores_gemma":[0.8273291,0.0001641484,0.167479,0.00005478166,0.00003449379,0.00009580595,0.003474692,0.0004137485,0.0009543339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01050207,"threshold_uncertainty_score":0.05554092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06506388906274672,"score_gpt":0.3127034517741774,"score_spread":0.2476395627114306,"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."}}