{"id":"W2920930972","doi":"10.1016/j.rse.2019.02.015","title":"Current status of Landsat program, science, and applications","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Remote sensing; Context (archaeology); Data quality; Earth observation; Data management; Earth system science; Computer science; Satellite; Environmental resource management; Data science; Environmental science; Geography; Database; Business; Geology; Engineering","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":[0.0001999118,0.0001333872,0.0001862583,0.00001637963,0.00006409144,0.000007535182,0.0001236337,0.00003639765,0.0001337517],"category_scores_gemma":[0.00000739731,0.0001229822,0.00003674403,0.0001815347,0.00112698,0.00008907628,0.0003182069,0.00008573067,0.00006516837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000292793,"about_ca_system_score_gemma":0.00001523172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001873303,"about_ca_topic_score_gemma":0.000002437523,"domain_scores_codex":[0.9984847,0.00001588518,0.0002486649,0.000349649,0.0005636799,0.0003374056],"domain_scores_gemma":[0.999293,0.00002028127,0.000162421,0.0003712165,0.000003810637,0.0001493094],"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":[0.000009651224,0.0001017697,0.03563531,0.00002797887,0.000006376834,3.561585e-7,0.0001199451,0.00911173,0.02295252,0.00003751172,0.000009191473,0.9319876],"study_design_scores_gemma":[0.001150109,0.000557364,0.5640681,0.00009466608,0.00008486655,0.00002142008,0.0004442599,0.1738475,0.006823778,0.0008874452,0.251379,0.0006415708],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854422,0.0002125763,0.00885673,0.00002039616,0.00004827309,0.0006419317,0.000002589651,0.00001718807,0.004758118],"genre_scores_gemma":[0.9161506,0.0009680559,0.08271834,0.00001738183,0.00001091332,5.97403e-7,0.000003093822,0.0000152824,0.0001157953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9313461,"threshold_uncertainty_score":0.5015069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005920521512526606,"score_gpt":0.2230714749227832,"score_spread":0.2171509534102566,"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."}}