{"id":"W4306195204","doi":"10.5194/isprs-archives-xlviii-4-w4-2022-21-2022","title":"TASK DECOMPOSITION AND LEVEL OF COMPLEXITY TO SELECT THE CONTENT OF UNDERGROUND UTILITY NETWORK MODEL","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Power Systems and Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Task (project management); Computer science; Selection (genetic algorithm); Decomposition; Representation (politics); Process (computing); Artificial intelligence; Engineering; Programming language; Systems engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001431475,0.0002749296,0.0003818438,0.000516246,0.001065369,0.0002457928,0.001247788,0.00004997923,0.000002901674],"category_scores_gemma":[0.000270922,0.0001704794,0.0002237716,0.0007933828,0.002288856,0.0002601544,0.00102858,0.0003502338,3.158386e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004362812,"about_ca_system_score_gemma":0.0001200662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5822867,"about_ca_topic_score_gemma":0.1280422,"domain_scores_codex":[0.9967113,0.0002126256,0.001192968,0.0002391592,0.001295456,0.0003484497],"domain_scores_gemma":[0.9976351,0.000711373,0.0009936971,0.0003721086,0.0002076229,0.00008005584],"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":[0.0001121239,0.00001427401,0.0002444312,0.0000487316,0.00009551399,7.973547e-8,0.002967254,0.0576188,0.003654102,0.00005560603,0.00006154535,0.9351276],"study_design_scores_gemma":[0.0003951844,0.0001316946,0.006182074,0.000165053,0.00002965312,0.00007146555,0.002736862,0.9730849,0.005294661,0.01095197,0.000778836,0.0001776911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06913143,0.00004711224,0.9243934,0.001872247,0.000949018,0.0005800343,0.0002308638,0.00003952558,0.002756358],"genre_scores_gemma":[0.9956573,0.00007742348,0.003817738,0.0003499422,0.00004199689,6.31716e-7,0.00002157725,0.000007771493,0.0000255922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9349499,"threshold_uncertainty_score":0.8878688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06404951704961315,"score_gpt":0.2715555451676424,"score_spread":0.2075060281180293,"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."}}