{"id":"W272189168","doi":"10.1177/0309133315582005","title":"Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data","year":2015,"lang":"en","type":"article","venue":"Progress in Physical Geography Earth and Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Remote sensing; Lidar; Hyperspectral imaging; Multispectral image; Environmental science; Biomass (ecology); Vegetation (pathology); Synthetic aperture radar; Satellite; Ecology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001978518,0.0002121263,0.0002666806,0.00005493608,0.00006365922,0.00003981321,0.0001473492,0.00007166593,0.000003907954],"category_scores_gemma":[0.0000156657,0.00017515,0.00004514629,0.0001759849,0.0007105193,0.0002105984,0.0002969919,0.0001809301,0.000005944881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002894662,"about_ca_system_score_gemma":0.000005440866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004162967,"about_ca_topic_score_gemma":0.00002122777,"domain_scores_codex":[0.9984345,0.00007869826,0.0002258366,0.0005559906,0.0003927943,0.0003121358],"domain_scores_gemma":[0.9992673,0.00003336151,0.0001164681,0.0003973804,0.000002651965,0.0001828317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002243599,0.0005723484,0.1778127,0.00006517558,0.00007379203,0.00006720176,0.002645288,0.004155988,0.04849023,0.000006123398,0.0000193312,0.7658675],"study_design_scores_gemma":[0.001846237,0.0003908614,0.4971032,0.0001764735,0.0001084536,0.00005602243,0.0002287975,0.4856281,0.01215928,0.001175437,0.0005741012,0.0005530106],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985774,0.0004712303,0.0002907591,0.00007426901,0.00006251004,0.0003358357,0.000005329293,0.00001491365,0.0001678158],"genre_scores_gemma":[0.9443194,0.0001564834,0.0554161,0.00001260232,0.0000683016,1.75122e-7,0.00001163464,0.00001331972,0.000001987178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7653145,"threshold_uncertainty_score":0.714241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179360647175564,"score_gpt":0.2483004292225226,"score_spread":0.226506822750767,"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."}}