{"id":"W3204630500","doi":"10.5194/isprs-archives-xlvi-4-w4-2021-137-2021","title":"A FIRST ATTEMPT TO DEFINE LEVEL OF DETAILS BASED ON DECISION-MAKING TASKS: APPLICATION TO UNDERGROUND UTILITY NETWORK","year":2021,"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":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"CityGML; Computer science; Relevance (law); Visualization; Key (lock); Process (computing); Data mining; Artificial intelligence; Machine learning; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001456628,0.0007105864,0.0004729092,0.001237796,0.0007157077,0.00241775,0.0008030518,0.0008629124,0.00473181],"category_scores_gemma":[0.006638399,0.0002885217,0.0008980078,0.001628722,0.0007832902,0.0019446,0.001830195,0.0009281196,0.0005116379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226621,"about_ca_system_score_gemma":0.0009871479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007597391,"about_ca_topic_score_gemma":0.007374443,"domain_scores_codex":[0.9989213,0.0004818539,0.00007310729,0.0001615376,0.0002938923,0.00006834865],"domain_scores_gemma":[0.9957873,0.002836082,0.0002448369,0.0004745613,0.0004909398,0.0001661741],"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":[0.0009818142,0.001130275,0.03454424,0.002172197,0.0001480179,0.001007436,0.01455137,0.1795025,0.04639462,0.1400314,0.00589254,0.5736436],"study_design_scores_gemma":[0.0000657287,0.0003912196,0.01503714,0.0002698559,0.0001223569,0.0004426531,0.006030449,0.8792303,0.01712641,0.04521932,0.03592001,0.0001445374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1718936,0.000285047,0.8059938,0.0005685367,0.00003853199,0.000444841,0.000458241,0.0009725269,0.01934491],"genre_scores_gemma":[0.5205377,0.0002044697,0.476305,0.0000364734,0.000008550397,0.0001549149,0.0002642981,0.0001006597,0.002387849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007597391,"threshold_uncertainty_score":0.01582944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02767110085170634,"score_gpt":0.2703090793742368,"score_spread":0.2426379785225304,"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."}}