{"id":"W2647979953","doi":"10.3390/ijgi6070193","title":"Spatial Context from Open and Online Processing (SCOOP): Geographic, Temporal, and Thematic Analysis of Online Information Sources","year":2017,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Geospatial analysis; Information retrieval; Cluster analysis; Context (archaeology); Information extraction; Thematic map; Automatic summarization; Data extraction; Data science; Data mining; Geography; Artificial intelligence; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001393553,0.0001631723,0.0004985246,0.001244156,0.0007838733,0.001649132,0.0009617745,0.0001156163,0.00005092744],"category_scores_gemma":[0.0009445055,0.0001378694,0.0001415573,0.0002585142,0.0004182532,0.01579877,0.0003139525,0.0001813429,0.000003645474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005688804,"about_ca_system_score_gemma":0.0001752701,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02377784,"about_ca_topic_score_gemma":0.008716472,"domain_scores_codex":[0.997026,0.00006929514,0.001564219,0.00007131459,0.001102215,0.0001669456],"domain_scores_gemma":[0.9924811,0.0001428675,0.004583379,0.0001791193,0.002506364,0.0001072225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003397574,0.0001222691,0.5097049,0.0001122349,0.002658002,0.000001913357,0.1114651,0.0002251829,0.00001005505,0.004049406,0.0003051556,0.371006],"study_design_scores_gemma":[0.002593281,0.0001341844,0.8437454,0.0006352959,0.0006009763,0.00001448629,0.1156513,0.0091119,0.00003071548,0.001752977,0.02540695,0.000322578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845023,0.0002201104,0.008651039,0.00342237,0.0005782276,0.0003882067,0.0005385359,0.00001711406,0.001682161],"genre_scores_gemma":[0.9972733,0.0005494861,0.001396728,0.0003624283,0.0001405238,0.0000047914,0.0002566679,0.000003302006,0.00001274255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3706834,"threshold_uncertainty_score":0.9993873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02355729412208724,"score_gpt":0.3382027309101093,"score_spread":0.3146454367880221,"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."}}