{"id":"W4317743668","doi":"10.3390/hydrology10020031","title":"Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast","year":2023,"lang":"en","type":"article","venue":"Hydrology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Environmental science; Water content; Downscaling; Topsoil; Moisture; In situ; Data assimilation; Streamflow; Watershed; Soil science; Remote sensing; Precipitation; Soil water; Atmospheric sciences; Meteorology; Geology; Drainage basin; Geography","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":[0.0002684765,0.00008636739,0.0001440618,0.00003870635,0.000114194,0.00001852743,0.00009364329,0.00009907281,0.000004209501],"category_scores_gemma":[0.00004136686,0.00006000368,0.00003960312,0.0001576557,0.0002137582,0.00007409103,0.00013145,0.0001116531,0.000005984282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002085804,"about_ca_system_score_gemma":0.000007847588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006136042,"about_ca_topic_score_gemma":0.005849407,"domain_scores_codex":[0.9992746,0.00004770136,0.0001503242,0.0002002954,0.00008687082,0.0002402585],"domain_scores_gemma":[0.9996454,0.0001188294,0.00007018886,0.0001340893,0.000004753485,0.00002679424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001292723,0.00007060411,0.0988403,0.00006535996,0.00003016358,0.00005519736,0.001261997,0.01328975,0.6344534,0.00005703644,0.0006228185,0.2511241],"study_design_scores_gemma":[0.000875439,0.0001262519,0.910124,0.000009581644,0.00003202469,0.00003621884,0.0004103255,0.07952458,0.005874649,0.002649107,0.0002176989,0.0001201297],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954305,0.0000286434,0.0002941642,0.0009501585,0.0001994133,0.0001634477,6.99514e-7,0.00002229932,0.002910692],"genre_scores_gemma":[0.999402,0.000005684233,0.0002783719,0.0001592493,0.00005144352,0.000002180388,0.000007397863,0.00001058531,0.00008307197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8112837,"threshold_uncertainty_score":0.3264107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045047994612127,"score_gpt":0.2396197541458352,"score_spread":0.2291692741997139,"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."}}