{"id":"W2625129035","doi":"10.1002/2017rg000562","title":"Validation practices for satellite‐based Earth observation data across communities","year":2017,"lang":"en","type":"article","venue":"Reviews of Geophysics","topic":"GNSS positioning and interference","field":"Engineering","cited_by":243,"is_retracted":false,"has_abstract":true,"ca_institutions":"Inversa Systems (Canada)","funders":"Deutsche Forschungsgemeinschaft; Natural Environment Research Council; Sight Research UK","keywords":"Terminology; Computer science; Satellite; Earth observation; Variety (cybernetics); Task (project management); Remote sensing; Earth observation satellite; Data mining; Data science; Systems engineering; Artificial intelligence; Geography; 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":[0.0003573758,0.00008341733,0.0001942966,0.000008354384,0.0001929833,0.0001276595,0.0005253163,0.00002942997,0.000004010606],"category_scores_gemma":[0.0002016642,0.00007950792,0.00004335483,0.00002432312,0.00003956757,0.0008095026,0.00005553999,0.00006995385,0.00001872188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006255478,"about_ca_system_score_gemma":0.000009858628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001417806,"about_ca_topic_score_gemma":0.00003316042,"domain_scores_codex":[0.999505,0.00003199949,0.0002226469,0.00007516106,0.00006697972,0.00009824103],"domain_scores_gemma":[0.9982619,0.0001128131,0.0004182084,0.001098184,0.00009351088,0.00001534501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006445488,0.0003202345,0.01067777,0.01084618,0.0001969316,3.776615e-7,0.003186936,0.003452187,0.03416087,0.006614793,0.003686713,0.9267926],"study_design_scores_gemma":[0.0007994766,0.0002259209,0.03856223,0.00271749,0.0002010227,0.000001137973,0.0003179553,0.2220712,0.06583701,0.001360569,0.6672606,0.0006454347],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485789,0.006420298,0.03636376,0.0002533729,0.0007094454,0.001123523,0.0005455679,0.0001663968,0.005838762],"genre_scores_gemma":[0.9856279,0.005160252,0.008190135,0.00003055944,0.00009142629,0.00003240181,0.0007743195,0.00001493711,0.00007808581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9261471,"threshold_uncertainty_score":0.3242239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3598690701300717,"score_gpt":0.4191450008262085,"score_spread":0.05927593069613679,"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."}}