{"id":"W2164178448","doi":"10.5194/isprsarchives-xxxix-b2-157-2012","title":"MULTI-CRITERIA PATH FINDING","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Shortest path problem; Computer science; Flexibility (engineering); Constrained Shortest Path First; Mathematical optimization; Path (computing); Enhanced Data Rates for GSM Evolution; Point (geometry); K shortest path routing; Longest path problem; Rank (graph theory); Algorithm; Graph; Mathematics; Theoretical computer science; Artificial intelligence; Computer network; Statistics; Combinatorics","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":[0.003044103,0.001881912,0.002331921,0.004476356,0.001641032,0.00247834,0.002234634,0.002576414,0.01236786],"category_scores_gemma":[0.009424766,0.0007770534,0.001762427,0.004141181,0.0009404356,0.00201187,0.002009068,0.001023135,0.001664038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001763653,"about_ca_system_score_gemma":0.001678704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00336638,"about_ca_topic_score_gemma":0.002617342,"domain_scores_codex":[0.9967354,0.001266562,0.000241378,0.0005623983,0.000931604,0.0002627465],"domain_scores_gemma":[0.995351,0.002790081,0.0003502365,0.0002136138,0.0011273,0.0001677184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002114764,0.0001903094,0.003027467,0.0007888365,0.0002582428,0.0002916978,0.0002321779,0.7506379,0.001978164,0.02663643,0.006047089,0.2097003],"study_design_scores_gemma":[0.00003293456,0.0001479953,0.0003816565,0.00006862083,0.00003584578,0.0001152388,0.00008710913,0.9751444,0.0009259104,0.0188407,0.00419306,0.00002649644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01631495,0.0007264679,0.9727816,0.0003263989,0.0001086274,0.00043317,0.0003552036,0.000321768,0.008631823],"genre_scores_gemma":[0.270893,0.0004001496,0.7194982,0.000170517,0.00006725731,0.0006730759,0.0007836979,0.0001569401,0.007357197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01236786,"threshold_uncertainty_score":0.04137456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283945565506333,"score_gpt":0.2779082881230395,"score_spread":0.2495137315724062,"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."}}