{"id":"W4240927840","doi":"10.5194/hessd-9-13995-2012","title":"McMaster Mesonet soil moisture dataset: description and spatio-temporal variability analysis","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Water content; Watershed; Moisture; Hydrology (agriculture); Atmospheric sciences; Geography; Meteorology; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004523687,0.001124451,0.0007914844,0.002254366,0.0007466279,0.00112078,0.0024292,0.0006469523,0.008981115],"category_scores_gemma":[0.001925979,0.0004237894,0.0006618179,0.006749,0.0003545347,0.0005476706,0.0009198218,0.0006519731,0.007227065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003859807,"about_ca_system_score_gemma":0.005743468,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7685545,"about_ca_topic_score_gemma":0.858175,"domain_scores_codex":[0.9994565,0.00003656631,0.00005096269,0.000135832,0.000220505,0.00009954951],"domain_scores_gemma":[0.9987472,0.00006893421,0.00009189033,0.0002283416,0.0007441218,0.0001194652],"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":[0.0003680766,0.00008058572,0.03547086,0.0007789221,0.0002380026,0.0002346578,0.0001706589,0.01102683,0.002366427,0.001169841,0.9199126,0.02818245],"study_design_scores_gemma":[0.0005947483,0.00005993534,0.1922438,0.0002676976,0.000117348,0.0001956244,0.0003883229,0.04529193,0.004127511,0.001557924,0.7549621,0.0001929767],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003782928,0.00007367248,0.000722096,0.00003560859,0.00001097607,0.00005594533,0.9929945,0.00144651,0.0008777337],"genre_scores_gemma":[0.009079347,0.00006345442,0.001998211,0.00001940323,0.000004498294,0.0001323099,0.9879814,0.0001186542,0.0006027722],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7685545,"threshold_uncertainty_score":0.465617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01938551357260503,"score_gpt":0.2371792189838371,"score_spread":0.217793705411232,"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."}}