{"id":"W2150280378","doi":"10.1038/nclimate1908","title":"The role of satellite remote sensing in climate change studies","year":2013,"lang":"en","type":"article","venue":"Nature Climate Change","topic":"Climate variability and models","field":"Environmental Science","cited_by":611,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Oceanic and Atmospheric Administration","keywords":"Climate change; Satellite; Remote sensing; Environmental science; Climate model; Temporal scales; Climatology; Earth system science; Scale (ratio); Meteorology; Geography; Geology; Oceanography; Cartography","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":[0.0009711769,0.0002475035,0.0003414409,0.00006240255,0.0002394231,0.00004238478,0.0002870749,0.0002891587,0.0001631894],"category_scores_gemma":[0.0001204674,0.0001724809,0.00009566172,0.0003842154,0.0003113598,0.0004283472,0.0006219054,0.0004828511,0.000238747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001790468,"about_ca_system_score_gemma":0.000002425802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008346878,"about_ca_topic_score_gemma":0.002132223,"domain_scores_codex":[0.9979139,0.0001242119,0.000415668,0.0004338369,0.0003384956,0.0007739058],"domain_scores_gemma":[0.9988698,0.0003000503,0.0001898964,0.0005151759,0.00004168024,0.0000833668],"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":[0.0001220204,0.0001371167,0.05232277,0.0003016184,0.0000395307,0.00001837483,0.02085992,0.00001415014,0.01820986,0.002214402,0.0001546729,0.9056056],"study_design_scores_gemma":[0.003467157,0.000676462,0.624936,0.002676393,0.000246813,0.00009209518,0.03332913,0.0683687,0.01626365,0.1295412,0.1170787,0.003323616],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9693075,0.01510468,0.000001078771,0.003221022,0.0004948013,0.001382431,0.00004650838,0.00007658978,0.01036541],"genre_scores_gemma":[0.9546485,0.04354427,0.0006851674,0.0008561114,0.0001763435,0.00003012311,0.00001085623,0.00002977366,0.00001883235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9022819,"threshold_uncertainty_score":0.7033567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04300857450566189,"score_gpt":0.2877051942609736,"score_spread":0.2446966197553117,"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."}}