{"id":"W2905581501","doi":"10.29173/iq914","title":"From Paper Map to Geospatial Vector Layer","year":2018,"lang":"en","type":"article","venue":"IASSIST Quarterly","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Geospatial analysis; USable; Computer science; Geographic information system; Process (computing); Raster graphics; Software; Raster data; Information retrieval; Data mining; Layer (electronics); Database; World Wide Web; Cartography; Geography; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001636464,0.0006486075,0.0003611933,0.004007015,0.00107197,0.01068729,0.001478991,0.0006653544,0.06160527],"category_scores_gemma":[0.007526507,0.0005311195,0.000571047,0.009471414,0.001041302,0.009776968,0.00415928,0.001891846,0.0298177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553162,"about_ca_system_score_gemma":0.002622485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115959,"about_ca_topic_score_gemma":0.006704153,"domain_scores_codex":[0.998882,0.0001820372,0.000103835,0.0001892799,0.0005498027,0.00009312626],"domain_scores_gemma":[0.9969189,0.0004470526,0.0001900961,0.001021003,0.001178727,0.0002442173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001676721,0.00007859037,0.004001838,0.000637694,0.000052144,0.0003515344,0.002696031,0.001604092,0.003369519,0.1433072,0.3270255,0.5167081],"study_design_scores_gemma":[0.00001618715,0.00002177037,0.001987119,0.0002060261,0.00001988566,0.00013749,0.001406373,0.001576586,0.004799192,0.02455736,0.9652337,0.00003828961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0243906,0.00291026,0.3862641,0.009915114,0.005329218,0.0009361167,0.03906313,0.03386356,0.4973279],"genre_scores_gemma":[0.1971272,0.0105463,0.477595,0.003380053,0.001273156,0.001141598,0.03702315,0.0170072,0.2549064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06160527,"threshold_uncertainty_score":0.2060902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856638854028787,"score_gpt":0.2965370144213555,"score_spread":0.2779706258810676,"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."}}