{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001242875,0.000399555,0.0004461492,0.0007496543,0.001007473,0.0004386726,0.001454599,0.00009291591,0.000008295407],"category_scores_gemma":[0.001036216,0.0002869813,0.0003146695,0.001579109,0.001075346,0.0002629789,0.0007327324,0.0003558661,0.000005734388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007333166,"about_ca_system_score_gemma":0.0002353253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3868268,"about_ca_topic_score_gemma":0.3054326,"domain_scores_codex":[0.9956691,0.0001421571,0.001532131,0.0004348293,0.001730361,0.0004913869],"domain_scores_gemma":[0.9958758,0.001894513,0.0008813899,0.0007133882,0.0004550008,0.0001798715],"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":[0.00009538194,0.00001755134,0.0001654348,0.00003854465,0.00004448047,1.216394e-7,0.0009775786,0.3073883,0.0008214576,0.00001311767,0.00006675275,0.6903713],"study_design_scores_gemma":[0.0004504691,0.0001012853,0.005272555,0.0006726352,0.0000372941,0.0000352072,0.0005509391,0.9764635,0.004512832,0.008907156,0.002704381,0.0002917681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0103243,0.00002031624,0.9804004,0.003240789,0.00113902,0.0008225822,0.0001943545,0.00005969244,0.003798514],"genre_scores_gemma":[0.9656537,0.00004571708,0.03271472,0.001393341,0.0001181166,0.000001564388,0.000038219,0.00001598819,0.00001864911],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9553294,"threshold_uncertainty_score":0.9999582,"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."}}