{"id":"W3206395325","doi":"10.1109/igarss47720.2021.9553515","title":"Random Forest Outperformed Convolutional Neural Networks for Shrub Willow Above Ground Biomass Estimation Using Multi-Spectral UAS Imagery","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"Honeywell Hometown Solutions; U.S. Department of Agriculture","keywords":"Willow; Shrub; Biomass (ecology); Vegetation (pathology); Environmental science; Convolutional neural network; Remote sensing; Random forest; Bioenergy; Hyperspectral imaging; Computer science; Artificial intelligence; Renewable energy; Agronomy; Engineering; Ecology; Geography","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":[0.0008896065,0.0009914681,0.0003781659,0.0006660723,0.0002099115,0.0003436767,0.0004305372,0.0004516594,0.0006465954],"category_scores_gemma":[0.001229037,0.0001865784,0.0005927947,0.0004426748,0.000145578,0.0006192895,0.0002175177,0.0004770562,0.0002580354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004231948,"about_ca_system_score_gemma":0.0005774106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04363472,"about_ca_topic_score_gemma":0.05129587,"domain_scores_codex":[0.9998266,0.00003774178,0.000009462424,0.00004922395,0.00002878989,0.00004816923],"domain_scores_gemma":[0.9996703,0.0001325555,0.00003505449,0.00003019959,0.0001145595,0.00001745827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005363878,0.0003208762,0.02339601,0.0001089455,0.0002538744,0.0001478422,0.00004162515,0.708792,0.01397387,0.0004914537,0.002180126,0.2497569],"study_design_scores_gemma":[0.000003902127,0.00003014616,0.002497496,0.000004884866,0.00001549436,0.000009037363,0.000008565908,0.9957095,0.001492062,0.00009375669,0.0001301851,0.000004977198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8974181,0.001563485,0.09460029,0.0003038559,0.0001719755,0.00004950467,0.0006508536,0.001998304,0.00324351],"genre_scores_gemma":[0.9793783,0.0001867833,0.01853263,0.00004549908,0.00001850185,0.00001154169,0.0007551714,0.00002716369,0.001044454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04363472,"threshold_uncertainty_score":0.08676147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350242934431211,"score_gpt":0.2654875577406962,"score_spread":0.241985128396384,"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."}}