{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001527679,0.0001206398,0.0001029058,0.00002406551,0.0006244233,0.00003779227,0.00003926241,0.00007905781,0.0002322502],"category_scores_gemma":[0.00005821505,0.00011769,0.00003443918,0.0001908917,0.00007599176,0.0001427427,0.00007250209,0.0003062622,0.0001255563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001699745,"about_ca_system_score_gemma":0.000007196901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003695652,"about_ca_topic_score_gemma":0.0001446992,"domain_scores_codex":[0.9991423,0.0001215847,0.0001588144,0.0002879488,0.0001162313,0.0001730517],"domain_scores_gemma":[0.9996453,0.0000597444,0.00009540598,0.00006496223,0.00001178261,0.0001228374],"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":[0.0001036378,0.00005296939,0.07219131,0.00001552055,0.00001327787,0.000003012985,0.0008260675,0.8333486,0.01986967,0.00006629891,0.001440183,0.07206946],"study_design_scores_gemma":[0.0002145315,0.00004072704,0.01920335,0.000006313044,0.0000201787,0.00005018722,0.00002063653,0.9469087,0.0001065747,0.00001819949,0.03328717,0.0001234463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8418395,0.000813457,0.1463411,0.001349432,0.0002410031,0.0001971383,0.000003372638,0.0002357436,0.00897927],"genre_scores_gemma":[0.9967031,0.00003052463,0.002085962,0.0000633426,0.000203319,0.000001583479,0.00007911879,0.00001525938,0.0008177969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1548636,"threshold_uncertainty_score":0.4802622,"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."}}