{"id":"W4306398684","doi":"10.3390/rs14205158","title":"Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Wildlife Fund Canada; Canadian Forest Service; Natural Resources Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Lidar; Environmental science; Canopy; Remote sensing; Tree canopy; Elevation (ballistics); Taiga; Satellite; Geography; Forestry; 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":[0.0002988307,0.0005244941,0.0002420654,0.00118453,0.0007107875,0.0006497446,0.0005130759,0.0002008132,0.0005180684],"category_scores_gemma":[0.0004846977,0.0001837788,0.0003282934,0.002382553,0.0002719725,0.0003974405,0.0004200447,0.0002414097,0.0001338459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008188491,"about_ca_system_score_gemma":0.009680373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9804556,"about_ca_topic_score_gemma":0.9884466,"domain_scores_codex":[0.9996946,0.0000126556,0.0000091453,0.00007234848,0.0001420625,0.00006925459],"domain_scores_gemma":[0.9995885,0.00002877568,0.00003015763,0.00001668092,0.0002942131,0.00004159496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007891795,0.0002947398,0.5842336,0.0004428775,0.0005234601,0.0005595947,0.0007887687,0.08178757,0.04097852,0.001134186,0.006758536,0.2817089],"study_design_scores_gemma":[0.00006168431,0.00009690774,0.8281383,0.00006659762,0.0002176286,0.0001283071,0.001092946,0.1540174,0.008673448,0.0002471202,0.007165647,0.00009398337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983771,0.0007449931,0.003504583,0.0000880779,0.00001275276,0.0000518484,0.007663453,0.0003559914,0.003807316],"genre_scores_gemma":[0.9845107,0.0003308027,0.008727267,0.00003574087,0.000003012191,0.00001236537,0.005401861,0.00002114801,0.0009570862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01954436,"threshold_uncertainty_score":0.05941188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00544133467861908,"score_gpt":0.1777437295631344,"score_spread":0.1723023948845153,"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."}}