{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002130223,0.0004504057,0.0004150026,0.001002665,0.001554352,0.001486149,0.003208964,0.00008398652,0.000008796832],"category_scores_gemma":[0.0006299876,0.0002844666,0.0003671712,0.001178446,0.002190635,0.001633822,0.00232921,0.0004278794,0.00000807757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004757095,"about_ca_system_score_gemma":0.0001606085,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6028103,"about_ca_topic_score_gemma":0.05306587,"domain_scores_codex":[0.9951569,0.000327011,0.001390228,0.0004387155,0.001960886,0.0007262281],"domain_scores_gemma":[0.996444,0.0008192342,0.001588176,0.0007002408,0.0002360896,0.0002122429],"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":[0.00004497358,0.0000308891,0.0003821021,0.00003427993,0.00007640065,2.911308e-7,0.003972637,0.0005313803,0.001514433,0.00003420091,0.0001088947,0.9932695],"study_design_scores_gemma":[0.0007084179,0.00009202362,0.00606079,0.0002634162,0.00003354018,0.0001062677,0.001318268,0.9757165,0.003957907,0.002856578,0.00854203,0.000344271],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003860872,0.00004377693,0.9809576,0.003812095,0.003476761,0.0005942033,0.00009051218,0.00007437797,0.007089833],"genre_scores_gemma":[0.9733139,0.0001428856,0.02473485,0.001395352,0.0002126722,4.260395e-7,0.00004128601,0.00001055191,0.0001480247],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9929252,"threshold_uncertainty_score":0.9999607,"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."}}