{"id":"W4415719733","doi":"10.3390/f16111658","title":"Detection of Vegetation Proximity to Power Lines: Critical Review and Research Roadmap","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Resilience (materials science); Reliability (semiconductor); Vegetation (pathology); Tree (set theory); Resource (disambiguation); Psychological resilience; Electric power system; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003535749,0.00003466363,0.00006635991,0.00003593882,0.00007829038,0.000008894977,0.00005158358,0.00002742482,0.00002063736],"category_scores_gemma":[0.0004564261,0.00003067822,0.00001311913,0.0003701099,0.0001114207,0.00004950257,0.00006030844,0.00007157027,0.00007361249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003328392,"about_ca_system_score_gemma":0.000007505071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001045804,"about_ca_topic_score_gemma":0.0005994093,"domain_scores_codex":[0.9994721,0.00004973849,0.0001057259,0.0001423616,0.0001357083,0.00009440153],"domain_scores_gemma":[0.9996881,0.00007332071,0.00001080605,0.0001535597,0.00003265791,0.00004159587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009659257,0.0006150146,0.07794996,0.003487733,0.00003800234,0.000004920892,0.002200504,0.000402752,0.2523673,0.01386699,0.02996344,0.6190068],"study_design_scores_gemma":[0.00009462277,0.0001344377,0.9496093,0.0005238272,0.00002543742,0.000004582114,0.00002788651,0.0008527614,0.01619751,0.009362916,0.02307565,0.00009104278],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612027,0.001369978,0.01070677,0.006276786,0.0000735861,0.0008240077,0.000001454709,0.000031887,0.01951284],"genre_scores_gemma":[0.99857,0.00005228281,0.0008984098,0.0001696753,0.000006057422,0.000008041356,6.502885e-7,0.000002580545,0.0002923512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8716593,"threshold_uncertainty_score":0.1251022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681058894212602,"score_gpt":0.3520205686452346,"score_spread":0.3252099797031086,"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."}}