{"id":"W4210502950","doi":"10.1016/j.rse.2022.112919","title":"Evaluating ICESat-2 for monitoring, modeling, and update of large area forest canopy height products","year":2022,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Compute Canada","keywords":"Taiga; Environmental science; Canopy; Remote sensing; Elevation (ballistics); Terrestrial ecosystem; Scale (ratio); Satellite; Tree canopy; Forest ecology; Boreal; Physical geography; Ecosystem; Geography; Forestry; Ecology; Cartography","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":[0.008956554,0.0008626345,0.0004083859,0.001778125,0.0005126074,0.001646557,0.001346746,0.0006345275,0.0005631466],"category_scores_gemma":[0.01353255,0.0002709905,0.0004752813,0.001999284,0.000271144,0.001534634,0.0006639507,0.0005163837,0.0004520327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002113392,"about_ca_system_score_gemma":0.002153092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2363447,"about_ca_topic_score_gemma":0.2771694,"domain_scores_codex":[0.9970973,0.0006595002,0.0001809766,0.0005243547,0.001312631,0.0002252401],"domain_scores_gemma":[0.9920822,0.001893193,0.0008105069,0.0008049675,0.003965145,0.000443974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001266957,0.0008254346,0.7155014,0.0002404116,0.0008606095,0.0002456944,0.000531145,0.1622911,0.0108953,0.001191701,0.0108984,0.0952519],"study_design_scores_gemma":[0.0001120157,0.0002225535,0.4328333,0.00009180211,0.0001438888,0.0000570227,0.0005703198,0.5537974,0.007077783,0.0002730884,0.004753653,0.00006718173],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678201,0.0004551405,0.01347028,0.0002025497,0.00006676184,0.0002967972,0.01162295,0.001698097,0.004367293],"genre_scores_gemma":[0.937438,0.0001814335,0.02980836,0.000103912,0.00001984504,0.0001136587,0.03144342,0.0001713354,0.000719921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2363447,"threshold_uncertainty_score":0.4699379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03454087466045587,"score_gpt":0.2848777190953311,"score_spread":0.2503368444348752,"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."}}