{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001831223,0.0006738272,0.0005626673,0.008315872,0.001341219,0.003464691,0.0009041193,0.0004579179,0.0009172851],"category_scores_gemma":[0.006705759,0.0003550458,0.001044303,0.009988548,0.0007865484,0.00376716,0.003862125,0.0009373316,0.0004616371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008694967,"about_ca_system_score_gemma":0.002324298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01107612,"about_ca_topic_score_gemma":0.01712482,"domain_scores_codex":[0.997771,0.0004338316,0.000270645,0.0003322951,0.001011303,0.0001809975],"domain_scores_gemma":[0.9966277,0.0009719465,0.0004173488,0.0008571682,0.000903207,0.0002225832],"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":[0.0004683372,0.0004173132,0.0534838,0.001950931,0.000460983,0.002887583,0.009521326,0.03086734,0.02243328,0.05587376,0.03153577,0.7900996],"study_design_scores_gemma":[0.00009892753,0.000198148,0.0681133,0.000707371,0.0003673592,0.002158769,0.02049828,0.468342,0.04294738,0.2024123,0.1938415,0.0003148062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1308329,0.002268818,0.8165495,0.001716029,0.0003039038,0.001599625,0.01682821,0.01542522,0.01447568],"genre_scores_gemma":[0.4301106,0.00111443,0.5493738,0.0001706817,0.0001439184,0.0006349227,0.01614086,0.0007341558,0.001576767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01107612,"threshold_uncertainty_score":0.02202332,"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."}}