{"id":"W2162425135","doi":"10.5194/hess-17-1589-2013","title":"McMaster Mesonet soil moisture dataset: description and spatio-temporal variability analysis","year":2013,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Environmental science; Water content; Watershed; Moisture; Hydrology (agriculture); Geography; Meteorology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008546583,0.0001262974,0.0002236109,0.0000660545,0.000407366,0.0001052907,0.0001091803,0.0001138575,0.0002485407],"category_scores_gemma":[0.00001739043,0.00008772074,0.00002999115,0.0003780421,0.0008446316,0.0003963671,0.0001126216,0.00008878312,0.00009627871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001251941,"about_ca_system_score_gemma":0.000006282145,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009842506,"about_ca_topic_score_gemma":0.009772344,"domain_scores_codex":[0.9986382,0.0002214151,0.0002018752,0.0004924982,0.0001936726,0.0002523111],"domain_scores_gemma":[0.9995683,0.00006463138,0.00009081871,0.0001624019,0.000006959359,0.000106817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007142162,0.00001580788,0.9900718,0.0000160045,0.00003765503,0.000003345718,0.0003791914,0.001140302,0.0003735696,0.0000629394,0.0005676221,0.007324568],"study_design_scores_gemma":[0.0001217322,0.00008322987,0.914308,0.000006762086,0.0001001141,0.00005490033,0.0003116751,0.08338209,0.00004060605,0.0002502111,0.001205729,0.0001350068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888384,0.00009433677,0.0004214953,0.000361724,0.0001055789,0.0001531277,0.000008167715,0.00002657451,0.009990616],"genre_scores_gemma":[0.998847,0.000006735728,0.0007116858,0.0002269142,0.00003510857,0.000002228034,0.000035104,0.000002597818,0.0001326344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08224179,"threshold_uncertainty_score":0.996751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128466047808121,"score_gpt":0.206560385317006,"score_spread":0.1952757248389247,"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."}}