{"id":"W2082472350","doi":"10.1016/j.rse.2009.07.003","title":"Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Spectroradiometer; Environmental science; Moderate-resolution imaging spectroradiometer; Nadir; Reflectivity; Deciduous; Bidirectional reflectance distribution function; Taiga; Vegetation (pathology); Land cover; Normalized Difference Vegetation Index; Leaf area index; Atmospheric correction; Canopy; Geography; Land use; Satellite; Forestry; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002332126,0.0004298037,0.0004508091,0.00009088044,0.0001994776,0.00006126129,0.0004677299,0.00007703956,0.0001225867],"category_scores_gemma":[0.00002853248,0.0003424515,0.0000936068,0.0004743702,0.0004447953,0.0004091873,0.0003999514,0.000339017,0.0001342717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472149,"about_ca_system_score_gemma":0.00001297968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001038301,"about_ca_topic_score_gemma":0.0003918522,"domain_scores_codex":[0.9969866,0.0001219858,0.0004158745,0.001043324,0.00078505,0.0006471627],"domain_scores_gemma":[0.9977444,0.00006002014,0.0003685664,0.001631847,0.0000077671,0.0001873481],"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":[0.0001189652,0.0004452652,0.02316772,0.00002701501,0.0001261725,0.0003669203,0.001142447,0.03043427,0.2178595,0.000001315513,0.002827798,0.7234826],"study_design_scores_gemma":[0.0008865517,0.0002186043,0.7773672,0.0001173518,0.00007780796,0.00024677,0.0001887731,0.1965043,0.001982443,0.00008204957,0.0215532,0.0007749663],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.899355,0.00006321783,0.09660301,0.0005754234,0.00007951181,0.0003862187,0.00001463947,0.00007914501,0.002843854],"genre_scores_gemma":[0.7396201,0.00005333943,0.2595617,0.0003479544,0.00008322504,2.290761e-8,0.00007427388,0.00003603083,0.0002232838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7541994,"threshold_uncertainty_score":0.9999027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251392409276465,"score_gpt":0.2447031505442011,"score_spread":0.2195639096165546,"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."}}