{"id":"W2133482720","doi":"10.1007/978-3-540-72665-4_46","title":"Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pathfinding; Computer science; Dijkstra's algorithm; Shortest path problem; Mathematical optimization; Motion planning; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Robot","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":[0.0002533524,0.0004222794,0.0004568797,0.000434212,0.0004386163,0.0003897638,0.0007120639,0.0003410924,0.005547433],"category_scores_gemma":[0.0009331226,0.0002845193,0.0003482354,0.0008094712,0.0002331147,0.0004891537,0.0006192367,0.0003438847,0.0005880058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006684615,"about_ca_system_score_gemma":0.001310683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04134408,"about_ca_topic_score_gemma":0.04431923,"domain_scores_codex":[0.9998537,0.00002632353,0.000007680057,0.00003550139,0.0000525096,0.00002418796],"domain_scores_gemma":[0.9998072,0.00008647511,0.000009418683,0.00002762366,0.00005590179,0.00001347259],"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":[0.0002448583,0.00008400623,0.001405977,0.0002666269,0.00004261896,0.0001408009,0.000421904,0.3162178,0.01997407,0.01100401,0.00689582,0.6433014],"study_design_scores_gemma":[0.00001874246,0.00007298579,0.001344056,0.00001786677,0.00002724007,0.00005084938,0.0000903739,0.9820704,0.005263676,0.007503435,0.003527204,0.0000132031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04676086,0.0003947011,0.9420495,0.00008928641,0.00007321925,0.0001435537,0.0002815228,0.003092898,0.007114492],"genre_scores_gemma":[0.4551382,0.0004769825,0.5373949,0.00003959879,0.00002314425,0.0001559023,0.0004415477,0.0001808305,0.006148982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04134408,"threshold_uncertainty_score":0.08220685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04220036228549223,"score_gpt":0.2939609903223324,"score_spread":0.2517606280368402,"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."}}