{"id":"W2952532736","doi":"10.1109/access.2019.2923765","title":"Computer Vision System for Automatic Counting of Planting Microsites Using UAV Imagery","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; McGill University; Université TÉLUQ","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Multispectral image; Convolutional neural network; Block (permutation group theory); Computer vision; Pattern recognition (psychology); Feature (linguistics); Process (computing); Tree (set theory); Remote sensing; Decision tree; Mathematics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003146077,0.0006475177,0.0005568646,0.00109622,0.0003120831,0.0004549545,0.0008283388,0.0006083118,0.004160151],"category_scores_gemma":[0.0005384948,0.0002696776,0.0003578886,0.000526725,0.0001473821,0.0005327929,0.0004442577,0.0005870605,0.002417715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004863453,"about_ca_system_score_gemma":0.0007108136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005096684,"about_ca_topic_score_gemma":0.008791961,"domain_scores_codex":[0.9997457,0.00001991354,0.00001225789,0.0001043825,0.0000809529,0.00003678981],"domain_scores_gemma":[0.9997661,0.00002326316,0.00002986263,0.00003154242,0.0001305589,0.00001870745],"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":[0.0003079855,0.0002438751,0.0039904,0.0002389894,0.00007789105,0.0002182204,0.00008006141,0.0160943,0.1569867,0.001328723,0.02082816,0.7996048],"study_design_scores_gemma":[0.00007497513,0.000264808,0.01947119,0.00005726589,0.00006900738,0.0003775687,0.00007716691,0.8696638,0.08925287,0.001873915,0.01875959,0.00005767751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06196446,0.0004632086,0.9064755,0.0001688955,0.0002014301,0.0004390905,0.001501348,0.0231065,0.005679452],"genre_scores_gemma":[0.3601665,0.000306894,0.6287302,0.0002639253,0.0000565962,0.0005553545,0.003117035,0.0002225408,0.006581025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005096684,"threshold_uncertainty_score":0.01391715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506933923124539,"score_gpt":0.2809857864929171,"score_spread":0.2659164472616717,"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."}}