{"id":"W3111643776","doi":"","title":"A SMAP validation sudy in the Canadian boreal forest","year":2019,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Metallurgy and Material Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Taiga; Environmental science; Boreal; Remote sensing; Climatology; Meteorology; Geology; Forestry; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002706201,0.0001067155,0.0001181281,0.00007017747,0.0001739909,0.0002685332,0.0004689453,0.00007713594,0.00004109311],"category_scores_gemma":[0.0002931606,0.00007557961,0.00002486215,0.000160561,0.00005093006,0.0002542425,0.00003006813,0.0001115616,0.00119477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006864712,"about_ca_system_score_gemma":0.0001850769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8288412,"about_ca_topic_score_gemma":0.9586793,"domain_scores_codex":[0.9985768,0.0001321977,0.000262536,0.0002681651,0.0003224813,0.0004378443],"domain_scores_gemma":[0.9993173,0.0001416449,0.000112865,0.0002768091,0.00003827954,0.0001130775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006991909,0.0001762094,0.5986534,0.0001203745,0.000007702086,0.0001913456,0.006469357,0.01330988,0.3683072,0.009964701,0.00135565,0.001374193],"study_design_scores_gemma":[0.0002115059,0.00004626181,0.9780074,0.00006092171,0.000005566347,0.00001466881,0.0002280506,0.0001059088,0.01550723,0.0009499406,0.00467831,0.0001841878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918682,0.000007619107,0.000001090914,0.0004749856,0.0006110451,0.0002297267,0.000002734589,0.00002492303,0.1067797],"genre_scores_gemma":[0.9991192,0.000001738817,0.0002438085,0.0003593921,0.00008466386,0.00001367953,0.00001325454,0.00000699695,0.0001572751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.379354,"threshold_uncertainty_score":0.9995829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164322213550854,"score_gpt":0.246116767396017,"score_spread":0.2244735452605084,"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."}}