{"id":"W2994976242","doi":"","title":"Alberta Soil Moisture Analyses using CaLDAS","year":2012,"lang":"en","type":"article","venue":"AGUFM","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Moisture; Environmental science; Water content; Soil science; Hydrology (agriculture); Geology; Geography; Meteorology; Geotechnical engineering","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":[0.000457042,0.000554301,0.0003408004,0.002642683,0.0009978543,0.0009869904,0.001044442,0.0002812256,0.01137978],"category_scores_gemma":[0.0008360644,0.0003362151,0.0004955883,0.003349799,0.0002097263,0.0003557738,0.0004695686,0.000447382,0.001912607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006354481,"about_ca_system_score_gemma":0.007339615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9428804,"about_ca_topic_score_gemma":0.974133,"domain_scores_codex":[0.9996269,0.00002016816,0.00001379972,0.00006278825,0.0002020245,0.00007435824],"domain_scores_gemma":[0.9994229,0.00002944247,0.00003594728,0.00004162132,0.0004310654,0.00003905652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006393289,0.0002508827,0.2331392,0.0003321878,0.0004412365,0.0005571797,0.00092348,0.08318985,0.02127847,0.009362826,0.2420847,0.4078006],"study_design_scores_gemma":[0.0002472306,0.00004734765,0.5362937,0.0001046571,0.0002433751,0.0001703444,0.0008282622,0.1747182,0.01713995,0.002800283,0.2672356,0.0001710672],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5207093,0.0008561198,0.07347624,0.0005352997,0.0002235699,0.0004589144,0.2404184,0.02067573,0.1426464],"genre_scores_gemma":[0.7215574,0.0006375873,0.122387,0.0002556256,0.00005123433,0.0003617492,0.08339508,0.002103387,0.06925091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05711961,"threshold_uncertainty_score":0.1149119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04611639481141172,"score_gpt":0.3105621140307153,"score_spread":0.2644457192193035,"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."}}