{"id":"W4412691076","doi":"10.22260/isarc2025/0152","title":"Developing Computer Vison-based Digital Twin for Vegetation Management Near Power Distribution Networks","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada First Research Excellence Fund","keywords":"Vegetation (pathology); Computer science; Distribution (mathematics); Power (physics); Telecommunications; Electrical engineering; Engineering; Mathematics","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":[0.0005969887,0.0005240441,0.0004691929,0.001463114,0.0004412426,0.001788896,0.001120045,0.0004789779,0.003088949],"category_scores_gemma":[0.002264339,0.0003469414,0.0005824657,0.001124996,0.0005894026,0.002703118,0.00237385,0.0006171847,0.0009073889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006944256,"about_ca_system_score_gemma":0.0007195714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005205613,"about_ca_topic_score_gemma":0.01038485,"domain_scores_codex":[0.9995932,0.0000612529,0.00002887561,0.0001060775,0.0001762991,0.00003431327],"domain_scores_gemma":[0.9994445,0.0001047921,0.00006040561,0.000139962,0.0001975278,0.00005276112],"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":[0.0002212298,0.0001510329,0.006798943,0.0002104908,0.00006815483,0.0003116239,0.0007252523,0.35089,0.02880475,0.04849739,0.006072006,0.5572491],"study_design_scores_gemma":[0.000005480661,0.00003686881,0.0007010785,0.00001600459,0.00001076156,0.00008381133,0.0001456426,0.9731434,0.008740441,0.008516532,0.00858172,0.00001816049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01586781,0.00003683075,0.9775487,0.00006935586,0.00002271476,0.00007352791,0.0001862472,0.002733728,0.003461136],"genre_scores_gemma":[0.2472806,0.0001413183,0.7478494,0.00005048892,0.00001555233,0.0001045691,0.0009124328,0.0003575641,0.003288071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005205613,"threshold_uncertainty_score":0.01035058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008409734380321294,"score_gpt":0.2129284847715925,"score_spread":0.2045187503912712,"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."}}