{"id":"W4244416877","doi":"10.21203/rs.3.rs-374682/v1","title":"Mauritia Flexuosa Palm-Trees Airborne Mapping with Deep Convolutional Neural Network","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universidade Federal de Mato Grosso do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"RGB color model; Palm; Convolutional neural network; Tree (set theory); Computer science; Artificial intelligence; Photogrammetry; Amazon rainforest; Remote sensing; Machine learning; Pattern recognition (psychology); Geography; Ecology; Mathematics; Biology","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":[0.0001641055,0.0006946427,0.0002497406,0.001035055,0.0002083901,0.0004986262,0.0005855469,0.0003912713,0.001355998],"category_scores_gemma":[0.0002809635,0.0001971916,0.0004440433,0.0005592459,0.0001369614,0.0004460744,0.0004700061,0.0003558833,0.0005894015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655245,"about_ca_system_score_gemma":0.0003301376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01386351,"about_ca_topic_score_gemma":0.01779909,"domain_scores_codex":[0.9998573,0.00001269436,0.000004550513,0.00006227971,0.00003442796,0.00002871641],"domain_scores_gemma":[0.9998848,0.00001949726,0.00001741681,0.00002560565,0.00003976887,0.00001277798],"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":[0.0002666158,0.0001863673,0.01343562,0.0001789327,0.0001307233,0.000333627,0.0001204321,0.1807683,0.0761134,0.001082461,0.004761506,0.722622],"study_design_scores_gemma":[0.000006916212,0.00003434354,0.01051133,0.00001899797,0.00002409326,0.0001243154,0.00006182079,0.9645426,0.02124675,0.0009820207,0.002430222,0.00001663925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5611632,0.001088269,0.4164539,0.0004142672,0.0001931043,0.0001152799,0.001749773,0.008952335,0.009869801],"genre_scores_gemma":[0.8809361,0.0001939179,0.1134645,0.00005760934,0.00002591914,0.00002440552,0.001109636,0.0000969735,0.004090896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01386351,"threshold_uncertainty_score":0.0275656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03949495050280002,"score_gpt":0.3157905668726109,"score_spread":0.2762956163698108,"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."}}