{"id":"W2008283931","doi":"10.1109/jstars.2015.2423272","title":"A Two-Level Approach for Species Identification of Coniferous Trees in Central Ontario Forests Based on Multispectral Images","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; York University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Computer science; Remote sensing; Spatial analysis; Multispectral pattern recognition; Artificial intelligence; Pattern recognition (psychology); Tree (set theory); Data mining; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002088263,0.0003625028,0.0001841417,0.002756832,0.0005429491,0.0007634811,0.0003683654,0.0003266278,0.0008133945],"category_scores_gemma":[0.0005260344,0.0001771376,0.0002571145,0.0008559169,0.0001818199,0.0005318832,0.0006260975,0.000179603,0.0001758358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037253,"about_ca_system_score_gemma":0.001067428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1027646,"about_ca_topic_score_gemma":0.2958522,"domain_scores_codex":[0.9998077,0.00001342332,0.00001040271,0.00005173959,0.0000726504,0.00004409147],"domain_scores_gemma":[0.9998354,0.00001572757,0.00002413151,0.00001247103,0.00008965482,0.00002265022],"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":[0.0003002294,0.0002440596,0.265326,0.0003379,0.000140338,0.0006334039,0.001787186,0.02035865,0.1319576,0.001830583,0.002204305,0.5748797],"study_design_scores_gemma":[0.00002432248,0.000111577,0.5929393,0.00005938276,0.0001388071,0.0003330279,0.002386791,0.3828398,0.01390134,0.001911363,0.005278969,0.00007537877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8788449,0.0005129716,0.1112137,0.0001875074,0.0000295317,0.0002006124,0.0009957742,0.0003863911,0.007628559],"genre_scores_gemma":[0.9403387,0.0001064259,0.05705107,0.00002290761,0.000007306136,0.00004518872,0.0004747538,0.00001199867,0.001941581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8972354,"threshold_uncertainty_score":0.2043328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04636561083716176,"score_gpt":0.2482310489545537,"score_spread":0.2018654381173919,"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."}}