{"id":"W3005415419","doi":"10.3390/make2010003","title":"Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"University of Regina","keywords":"Random forest; Remote sensing; Pixel; Convolutional neural network; Canopy; Computer science; Lidar; Environmental science; Satellite imagery; Geography; Artificial intelligence","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":[0.000215119,0.0006051371,0.0002678628,0.0008794349,0.0001786627,0.000418409,0.0005214988,0.000492017,0.001472384],"category_scores_gemma":[0.0006306929,0.0002387303,0.0004362125,0.0008449036,0.0001066946,0.0006045696,0.0002271384,0.0004001256,0.000604334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000606932,"about_ca_system_score_gemma":0.0003599552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03237626,"about_ca_topic_score_gemma":0.04623325,"domain_scores_codex":[0.999854,0.00001227396,0.00000699476,0.00004055129,0.00004841861,0.00003770335],"domain_scores_gemma":[0.9998242,0.00003421877,0.00002769921,0.00002870305,0.00007444253,0.0000107679],"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":[0.0004058561,0.0004609978,0.0297743,0.0002964298,0.0003995008,0.0004114592,0.00007550091,0.4619859,0.0371782,0.00158093,0.01151783,0.455913],"study_design_scores_gemma":[0.000006492725,0.00001870516,0.01235233,0.00001246015,0.00002386956,0.0000409887,0.00001749578,0.9804561,0.005798573,0.0005011694,0.0007571002,0.00001463381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7202118,0.001946786,0.2533392,0.000428334,0.0002066814,0.0001154074,0.007755657,0.007675407,0.008320721],"genre_scores_gemma":[0.9376402,0.0002958266,0.05431587,0.00008267278,0.00002696019,0.00002141098,0.005471426,0.0000705141,0.002075044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03237626,"threshold_uncertainty_score":0.06437564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660234135065939,"score_gpt":0.2684972853422704,"score_spread":0.251894943991611,"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."}}