{"id":"W2159686512","doi":"10.5194/gmd-5-413-2012","title":"Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data","year":2012,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"National Centre for Earth Observation; Natural Environment Research Council; Consortium of International Agricultural Research Centers; Government of the United Kingdom; University of Nottingham; Sight Research UK; Ohio State University","keywords":"Vegetation (pathology); Lidar; Altimeter; Environmental science; Remote sensing; Elevation (ballistics); Terrain; Satellite; Atmospheric sciences; Meteorology; Physical geography; Geology; Geography; Geometry; Mathematics","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":[0.0003204968,0.00044486,0.0002990487,0.001802355,0.000226999,0.000475745,0.0004096434,0.0002901794,0.002483502],"category_scores_gemma":[0.0006784038,0.00020072,0.0004746439,0.001300919,0.000195946,0.0004616003,0.0004211257,0.000288133,0.0008833463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005515011,"about_ca_system_score_gemma":0.0004431714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02499571,"about_ca_topic_score_gemma":0.05066451,"domain_scores_codex":[0.9997874,0.00001775665,0.00001576053,0.00006086583,0.00008729826,0.00003098525],"domain_scores_gemma":[0.9995295,0.00006753305,0.00009955812,0.0001125046,0.000148326,0.00004249105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008423027,0.0004094995,0.7750512,0.0003688164,0.0003761844,0.0005787422,0.0006401842,0.03752141,0.04175575,0.001147919,0.02566402,0.115644],"study_design_scores_gemma":[0.00003709644,0.00003593426,0.9627988,0.00003956997,0.00004365022,0.000113563,0.0001281879,0.01510059,0.007023001,0.0004150341,0.01421788,0.00004658544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8080687,0.0001949232,0.00846988,0.00007907616,0.00004288412,0.0001225701,0.1707229,0.0008654561,0.01143361],"genre_scores_gemma":[0.7746921,0.0001341218,0.02316635,0.0001146675,0.00003255672,0.0001935499,0.1997968,0.0001235655,0.001746247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02499571,"threshold_uncertainty_score":0.04970044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04104140956947406,"score_gpt":0.2521811715242077,"score_spread":0.2111397619547337,"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."}}