{"id":"W2560814041","doi":"","title":"Improvements of Physically-Based Hydrological Modelling using the ACRU Agro-Hydrological Modelling System","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Environmental science; Hydrology (agriculture); Hydrological modelling; Geology; Climatology; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00129077,0.0003168137,0.0004411601,0.0000360157,0.0004268858,0.00004864632,0.0004675316,0.0002191995,0.000005477677],"category_scores_gemma":[0.0000506657,0.0001989349,0.0002202319,0.0001840343,0.0002948485,0.0001020487,0.0002705533,0.0003696288,0.0000579692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001330323,"about_ca_system_score_gemma":0.0000115597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003691002,"about_ca_topic_score_gemma":0.0001283575,"domain_scores_codex":[0.997229,0.0003316767,0.000572551,0.0006167776,0.0006248633,0.000625156],"domain_scores_gemma":[0.9986281,0.0003401721,0.0003796504,0.0004967022,0.00002850523,0.0001268489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002843861,0.00005619091,0.005110096,0.00003026304,0.00001602346,0.000003675163,0.0001560048,0.9571463,0.03536374,0.00008933519,0.00001775447,0.001982167],"study_design_scores_gemma":[0.0003398582,0.0001009771,0.0003345908,0.0001116394,0.00006107308,0.000006260164,0.00009791185,0.9944201,0.003813096,0.000328346,0.000139844,0.0002462804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7287512,0.00003260591,0.2621666,0.00007803548,0.0001366558,0.0002156848,6.450612e-7,0.00008144543,0.008537071],"genre_scores_gemma":[0.9777963,0.000003848909,0.0215521,0.0003488058,0.0002439873,0.000002541013,0.000003418322,0.00003236084,0.00001662647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2490451,"threshold_uncertainty_score":0.8112329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02092402752798723,"score_gpt":0.2205264873962912,"score_spread":0.199602459868304,"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."}}