{"id":"W4403647388","doi":"10.3390/drones8110605","title":"Visual Servoing for Aerial Vegetation Sampling Systems","year":2024,"lang":"en","type":"article","venue":"Drones","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Visual servoing; Vegetation (pathology); Sampling (signal processing); Computer vision; Artificial intelligence; Computer science; Geography; Remote sensing; Environmental science; Image (mathematics); Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001230442,0.0002758037,0.000175483,0.0001491947,0.0001763913,0.0002521569,0.0004557725,0.0003024724,0.00133552],"category_scores_gemma":[0.0003741962,0.0001118344,0.0001375982,0.0001271701,0.0002418931,0.0002821893,0.0003525596,0.0003824791,0.0002227702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003969956,"about_ca_system_score_gemma":0.0003818719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004669866,"about_ca_topic_score_gemma":0.004589868,"domain_scores_codex":[0.9998863,0.000008815228,0.000005131159,0.00003606782,0.00004907675,0.00001461175],"domain_scores_gemma":[0.999903,0.00002383818,0.00002253091,0.00001183917,0.00003112817,0.000007747572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001733116,0.00007597142,0.00110598,0.0002143708,0.00002674351,0.0001654887,0.0001583654,0.3360617,0.2184794,0.007839733,0.002135213,0.4335638],"study_design_scores_gemma":[0.00001196404,0.00009454868,0.0006903505,0.00001225996,0.000005668491,0.00005477529,0.0000127636,0.9761699,0.01737648,0.00134115,0.004221576,0.000008578951],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04294662,0.0004394812,0.9485776,0.0001304156,0.00007206273,0.00004706209,0.00003601445,0.001078111,0.006672666],"genre_scores_gemma":[0.9218847,0.0001851323,0.07254715,0.00008893715,0.00002486257,0.00004100931,0.00005132891,0.00002985552,0.005147031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004669866,"threshold_uncertainty_score":0.00928539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02057664746818435,"score_gpt":0.2921208268810158,"score_spread":0.2715441794128314,"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."}}