{"id":"W3080437446","doi":"10.3390/ijgi9090497","title":"The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Volunteered geographic information; Social media; Metric (unit); Index (typography); Data science; Computer science; Scale (ratio); Citizen science; Spatial analysis; Information retrieval; Data mining; Quality (philosophy); Data quality; Geography; Crowdsourcing; Cartography; World Wide Web; Remote sensing","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":[0.01468021,0.0009003875,0.001392792,0.01179641,0.001166168,0.004399862,0.001507548,0.001268637,0.001086035],"category_scores_gemma":[0.06934472,0.0004522255,0.001322182,0.01577171,0.001560479,0.003777837,0.005026453,0.0009725333,0.000342909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001771004,"about_ca_system_score_gemma":0.002544651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0126599,"about_ca_topic_score_gemma":0.01970238,"domain_scores_codex":[0.9893288,0.003723979,0.001595274,0.002313434,0.002548752,0.0004897717],"domain_scores_gemma":[0.9580863,0.02490816,0.005682482,0.00603257,0.004492164,0.000798286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004233358,0.000191219,0.6703578,0.001872209,0.001033269,0.0005304582,0.004382585,0.08557542,0.003934787,0.03192713,0.01919046,0.1805813],"study_design_scores_gemma":[0.0001513589,0.0003528168,0.3703206,0.001016541,0.0004802753,0.00131026,0.008547997,0.4174817,0.007111982,0.1338615,0.05906016,0.0003047574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.473137,0.002702611,0.4619839,0.001738554,0.0001092267,0.001205088,0.0459836,0.002153125,0.0109869],"genre_scores_gemma":[0.7483801,0.0005096888,0.2198595,0.0001677005,0.0000623663,0.0009143125,0.02920602,0.0002517022,0.0006486009],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01468021,"threshold_uncertainty_score":0.07763726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203950054997477,"score_gpt":0.2769939827941363,"score_spread":0.2649544822441616,"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."}}