{"id":"W1984047080","doi":"10.1016/j.rse.2012.01.007","title":"Generation of a novel 1km NDVI data set over Canada, the northern United States, and Greenland based on historical AVHRR data","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Canadian Forest Service; Canadian Space Agency; University of British Columbia; National Oceanic and Atmospheric Administration; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Government of Canada","keywords":"Advanced very-high-resolution radiometer; Remote sensing; Normalized Difference Vegetation Index; Environmental science; Atmospheric correction; Satellite; Data set; Radiometry; Bidirectional reflectance distribution function; Geolocation; Meteorology; Geology; Climate change; Geography; Computer science; Reflectivity","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005272885,0.0002070078,0.000217431,0.00002833499,0.0001165895,0.00001143348,0.000399988,0.00007758753,0.00003169315],"category_scores_gemma":[0.00007477676,0.0001404831,0.00002027201,0.0001352034,0.0001784371,0.0001260276,0.000563501,0.000174928,0.000004254438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007510218,"about_ca_system_score_gemma":0.00002954079,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.408913,"about_ca_topic_score_gemma":0.1542538,"domain_scores_codex":[0.9980735,0.0001265889,0.0003292301,0.0004404934,0.0007440898,0.0002860612],"domain_scores_gemma":[0.9977382,0.0001364876,0.0002594849,0.001733123,0.000006261864,0.0001264841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002328992,0.0008553811,0.04391105,0.0001106083,0.0002390255,0.00003897444,0.002023186,0.3849558,0.2042591,0.000006181372,0.1686856,0.1946823],"study_design_scores_gemma":[0.0003834089,0.00004272693,0.06424771,0.0000315512,0.00006668383,0.00001858011,0.00003735002,0.8470462,0.0008030551,0.000002213517,0.08712333,0.0001971858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884661,0.0001083686,0.008935315,0.001412552,0.000214967,0.0002922947,0.0003319836,0.00001013771,0.0002283002],"genre_scores_gemma":[0.9756771,0.0000962372,0.02154969,0.000553165,0.0001519713,2.243351e-8,0.001716218,0.00003106066,0.0002244986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4620904,"threshold_uncertainty_score":0.8611789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0623066598958801,"score_gpt":0.2345677166670011,"score_spread":0.172261056771121,"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."}}