{"id":"W1901970062","doi":"10.3137/ao1112.2010","title":"Potential and limitations of using satellite data to evaluate the spatial detail in climatological air temperature maps","year":2010,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Aeronautics and Space Administration","keywords":"Satellite; Environmental science; Remote sensing; Land cover; Smoothing; Meteorology; Interpolation (computer graphics); Thermal infrared; Smoothing spline; Spline (mechanical); Infrared; Land use; Spline interpolation; Geography; Computer science; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02448603,0.0004599449,0.0004610427,0.002770482,0.0004171403,0.002061537,0.0009412226,0.0005403112,0.0007673826],"category_scores_gemma":[0.07393336,0.000325465,0.0005271776,0.00506621,0.0006735015,0.002051595,0.001086719,0.0005104661,0.0002698633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007984176,"about_ca_system_score_gemma":0.001028001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02955661,"about_ca_topic_score_gemma":0.03891634,"domain_scores_codex":[0.9876743,0.008450099,0.0009278335,0.0004981667,0.00222782,0.0002217916],"domain_scores_gemma":[0.928919,0.04113386,0.00639631,0.008854883,0.01405801,0.0006379822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005272819,0.00009564478,0.7850177,0.0006373124,0.0006900217,0.0001376194,0.001122234,0.04228807,0.004165219,0.003406238,0.001727553,0.1601851],"study_design_scores_gemma":[0.00006698658,0.0004915609,0.8378366,0.0007274554,0.0003492887,0.0004540021,0.002924558,0.1250552,0.00785988,0.007786174,0.01630036,0.0001479761],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9089314,0.004479575,0.06088649,0.001570466,0.0001610664,0.0002633157,0.005334775,0.000327278,0.01804562],"genre_scores_gemma":[0.9640609,0.0005774586,0.03340102,0.0001179447,0.00008043744,0.0001016889,0.0009065889,0.0000384158,0.0007154718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02955661,"threshold_uncertainty_score":0.1294961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06176788126549114,"score_gpt":0.2625469473826424,"score_spread":0.2007790661171512,"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."}}