{"id":"W4408384305","doi":"10.5194/egusphere-2025-893","title":"Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Oceanic and Atmospheric Administration","keywords":"Geospatial analysis; Streamflow; Forcing (mathematics); Environmental science; Drainage basin; Climatology; Geography; Hydrology (agriculture); Geology; Remote sensing; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001803493,0.0004265098,0.000499228,0.002697445,0.0005596651,0.0008374039,0.0008807593,0.0002427785,0.004986437],"category_scores_gemma":[0.007249639,0.00025938,0.0004753597,0.004196202,0.0002848157,0.0007788586,0.001166532,0.000457566,0.001201323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001390786,"about_ca_system_score_gemma":0.002405696,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0732134,"about_ca_topic_score_gemma":0.09349564,"domain_scores_codex":[0.9989542,0.0002461635,0.0001331581,0.0002019531,0.0004026959,0.00006183841],"domain_scores_gemma":[0.9936732,0.001151285,0.001155615,0.001525171,0.001826903,0.0006678102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004665758,0.0005546519,0.7302673,0.0003450882,0.0004012207,0.0002979923,0.0006321452,0.009885006,0.006178729,0.002237137,0.163824,0.08491019],"study_design_scores_gemma":[0.0001846719,0.00007436104,0.9363729,0.00005371211,0.00005729436,0.00009109237,0.0003278862,0.01197691,0.00224521,0.0008498167,0.04771882,0.00004734656],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3474762,0.0001013182,0.01158682,0.0003050583,0.00005189011,0.0007574199,0.6310473,0.002707983,0.005966044],"genre_scores_gemma":[0.3095753,0.00008145748,0.02847685,0.0001257397,0.00004429121,0.002250205,0.6577883,0.0002897131,0.001368182],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9267866,"threshold_uncertainty_score":0.1455745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08518549153047418,"score_gpt":0.3359711691786282,"score_spread":0.2507856776481541,"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."}}