{"id":"W4414268485","doi":"10.22541/essoar.175769276.62716087/v1","title":"Improvement of the PDI Model for Regional Hydrological Modeling and Land Surface Process Simulation","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Alberta","funders":"","keywords":"Surface runoff; Hydric soil; Soil and Water Assessment Tool; Wetland; Latent heat; Hydrology (agriculture); Eddy covariance; Surface water; Soil water; Precipitation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008509569,0.0004746515,0.0004258444,0.0002519097,0.0002679954,0.0006100191,0.001236334,0.0003787565,0.00160332],"category_scores_gemma":[0.001807811,0.0002542706,0.0003741457,0.0004637296,0.0002808216,0.0006981056,0.0006919615,0.0008062503,0.0003705911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007562613,"about_ca_system_score_gemma":0.001449537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02475398,"about_ca_topic_score_gemma":0.01160893,"domain_scores_codex":[0.9998111,0.00004911468,0.00001374625,0.00004582198,0.0000548727,0.00002540631],"domain_scores_gemma":[0.9996808,0.00006946849,0.00002279534,0.00007499338,0.0001118462,0.0000401498],"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":[0.00004971158,0.00007732934,0.005976275,0.00002545706,0.00002741525,0.00004028371,0.00003655532,0.9653024,0.003392539,0.003922061,0.001042496,0.02010744],"study_design_scores_gemma":[0.0000108274,0.000007191256,0.0003995112,9.247736e-7,0.000002586905,0.000003244619,0.000003125552,0.9984163,0.0003619898,0.0003625037,0.0004287321,0.00000311566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.532432,0.0001763551,0.4385903,0.0007274328,0.0001774177,0.0002260998,0.004452162,0.006436617,0.01678154],"genre_scores_gemma":[0.920629,0.00006173245,0.0759766,0.00005863235,0.00002652311,0.000135744,0.001537877,0.0003244799,0.001249448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02475398,"threshold_uncertainty_score":0.04921979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719245401833421,"score_gpt":0.2814571881462948,"score_spread":0.2442647341279606,"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."}}