{"id":"W2610288052","doi":"10.5623/cig2016-401","title":"mR-V: Line Simplification through Mnemonic Rasterization","year":2016,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Raster graphics; Context (archaeology); Line (geometry); Discretization; Computer science; Generalization; Line segment; Grid; Interpolation (computer graphics); Space (punctuation); Process (computing); Algorithm; Computer graphics (images); Topology (electrical circuits); Mathematics; Artificial intelligence; Geometry; Geography; Image (mathematics); Combinatorics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000845094,0.001587255,0.001220619,0.002343712,0.0009300165,0.00266498,0.00408825,0.0008932371,0.05798887],"category_scores_gemma":[0.006246389,0.001310119,0.001541314,0.002970713,0.0005351168,0.002450876,0.003040749,0.001711601,0.01644012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006962795,"about_ca_system_score_gemma":0.001349592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256408,"about_ca_topic_score_gemma":0.01587402,"domain_scores_codex":[0.9990137,0.0001492364,0.00009150395,0.000244656,0.0003860169,0.0001150079],"domain_scores_gemma":[0.9981868,0.0004327339,0.000115951,0.0007678369,0.0004205726,0.00007627626],"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":[0.0005308587,0.0001782439,0.00219488,0.0005080114,0.0002845221,0.0003264026,0.0006148343,0.03382804,0.01456311,0.03495776,0.1285903,0.7834231],"study_design_scores_gemma":[0.0003120929,0.0001468894,0.001531935,0.0001087912,0.0001602275,0.0005410882,0.0003694232,0.6758424,0.06559229,0.03525742,0.2199979,0.0001394763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004746431,0.00007378759,0.8495688,0.0001205294,0.000126766,0.0001329268,0.002579621,0.1383266,0.00432449],"genre_scores_gemma":[0.04609939,0.0001434444,0.9262137,0.00009246725,0.00005724058,0.0001766918,0.006366392,0.01585485,0.004995866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05798887,"threshold_uncertainty_score":0.1939921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03619636009200511,"score_gpt":0.3110749897770804,"score_spread":0.2748786296850753,"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."}}