{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004714037,0.001117466,0.0007961122,0.002286869,0.0007550746,0.001173836,0.002530555,0.0006394367,0.009359104],"category_scores_gemma":[0.002112251,0.0004313288,0.0006577561,0.006988498,0.0003619362,0.000588182,0.0009613758,0.0006727573,0.007560081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003875337,"about_ca_system_score_gemma":0.006135483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7718078,"about_ca_topic_score_gemma":0.8599383,"domain_scores_codex":[0.9994574,0.00003811374,0.00005392819,0.0001382255,0.0002154669,0.00009686554],"domain_scores_gemma":[0.9986418,0.00007756111,0.0000985209,0.000242331,0.0008160946,0.0001236361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003653776,0.000073724,0.03361377,0.0007638599,0.0002272547,0.0002254711,0.0001727493,0.01009229,0.002192626,0.001200976,0.9240407,0.02703122],"study_design_scores_gemma":[0.0006225355,0.00005603477,0.1735649,0.0002721369,0.0001149274,0.0001864112,0.0003854832,0.04550508,0.004097461,0.001656553,0.7733425,0.0001960523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003467368,0.00007151192,0.0007945253,0.00003589879,0.00001078,0.00005711827,0.9931223,0.00156933,0.0008712458],"genre_scores_gemma":[0.009034544,0.00006530375,0.002322451,0.00002065845,0.000004731228,0.0001454836,0.9876582,0.000138547,0.0006099019],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7718078,"threshold_uncertainty_score":0.459072,"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."}}