{"id":"W2242664426","doi":"10.11834/jrs.20143133","title":"Retrieving forest background reflectance in northeast of China from MODIS BRDF data: Taking Jiagedaqi District as a case study","year":2014,"lang":"en","type":"article","venue":"National Remote Sensing Bulletin","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Priority Academic Program Development of Jiangsu Higher Education Institutions; University of Toronto; National Aeronautics and Space Administration","keywords":"Bidirectional reflectance distribution function; Reflectivity; Remote sensing; Environmental science; China; Scale (ratio); Geography; Cartography; Archaeology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005364533,0.000495402,0.0003735153,0.001133091,0.0005436129,0.0007651213,0.0005469264,0.000437715,0.0003885823],"category_scores_gemma":[0.0004713473,0.0002760742,0.0005460961,0.001995072,0.0002464003,0.0004550831,0.0003265924,0.0001568704,0.0001482117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001101526,"about_ca_system_score_gemma":0.001430595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2578472,"about_ca_topic_score_gemma":0.3020661,"domain_scores_codex":[0.9998104,0.00001931452,0.00001491534,0.00005306469,0.00003659092,0.00006573802],"domain_scores_gemma":[0.9998176,0.00002889167,0.00002605803,0.00002651224,0.00006001213,0.00004086629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003057869,0.000305604,0.8413912,0.000191814,0.0001929821,0.002208815,0.0007430836,0.06175915,0.0252408,0.0004237049,0.002032426,0.06520469],"study_design_scores_gemma":[0.00004332251,0.00004468186,0.8946866,0.00001817135,0.000143647,0.0001440484,0.001095942,0.1001915,0.002533875,0.0001348686,0.0009261696,0.00003701713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985669,0.0001142707,0.0003324257,0.00003719473,0.000004021979,0.000007218432,0.0004302218,0.00003128966,0.0004763548],"genre_scores_gemma":[0.9973677,0.0001236216,0.001091764,0.00001077466,0.000004461773,0.00000459116,0.001060167,0.000005449439,0.0003314368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2578472,"threshold_uncertainty_score":0.5126927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03151067536038305,"score_gpt":0.2887032045807147,"score_spread":0.2571925292203316,"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."}}