{"id":"W3120844761","doi":"10.1007/s10707-020-00429-4","title":"Hidden Markov map matching based on trajectory segmentation with heading homogeneity","year":2021,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Map matching; Global Positioning System; Hidden Markov model; Computer science; Segmentation; Matching (statistics); Artificial intelligence; Trajectory; Markov chain; Markov model; Heading (navigation); Pattern recognition (psychology); Geography; Mathematics; Machine learning; Statistics; Geodesy","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":[0.0005694094,0.0005327384,0.001049316,0.001582355,0.0007183596,0.0007508845,0.001134498,0.0008171892,0.001943394],"category_scores_gemma":[0.002014058,0.0005232916,0.001037239,0.002555523,0.000403104,0.001139637,0.001152182,0.0008476158,0.001337094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005194794,"about_ca_system_score_gemma":0.001795196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01369257,"about_ca_topic_score_gemma":0.01179505,"domain_scores_codex":[0.9992887,0.00009164819,0.00003649461,0.0002811081,0.0001618632,0.0001401806],"domain_scores_gemma":[0.9992535,0.0002923613,0.00008312416,0.0001457929,0.0001724371,0.00005274218],"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":[0.001216252,0.0002998929,0.009190364,0.0001829884,0.0002937015,0.0003522331,0.0002411429,0.2664471,0.0268188,0.01134621,0.007091512,0.6765198],"study_design_scores_gemma":[0.00001748432,0.0000395288,0.001908172,0.000007346287,0.00004109005,0.00007025935,0.00002769336,0.9866717,0.005679303,0.004560289,0.0009612222,0.00001585969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04367737,0.0001572094,0.9531532,0.00009651521,0.00008535661,0.00005365859,0.0003690704,0.001459095,0.000948461],"genre_scores_gemma":[0.7040979,0.000207052,0.2882473,0.00008336446,0.00009740271,0.0001139729,0.0027811,0.0002529561,0.004119019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01369257,"threshold_uncertainty_score":0.02722573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061293708920493,"score_gpt":0.2234552142943787,"score_spread":0.2128422772051738,"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."}}