{"id":"W4404484879","doi":"10.1038/s41597-024-03994-7","title":"Historical datasets (1950–2022) of monthly water balance components for the Laurentian Great Lakes","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Oceanic and Atmospheric Administration; Environment and Climate Change Canada","keywords":"Water balance; Balance (ability); Bayesian probability; Environmental science; Psychological resilience; Reliability (semiconductor); Water resources; Ecosystem; Environmental resource management; Computer science; Ecology; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001066478,0.0001085128,0.0001286996,0.00004332602,0.0004662446,0.00009934113,0.001414652,0.00002974966,0.0009574878],"category_scores_gemma":[0.00003033873,0.00005956278,0.00004056972,0.0001736185,0.0005604977,0.0003869963,0.001861133,0.00007620738,0.000443789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005911639,"about_ca_system_score_gemma":0.000003506263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001821246,"about_ca_topic_score_gemma":0.0005203605,"domain_scores_codex":[0.9985299,0.00003290441,0.0001920851,0.0006529615,0.0002954838,0.000296654],"domain_scores_gemma":[0.9985824,0.00007395388,0.00002756073,0.001273251,0.000004576123,0.00003827153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000133184,0.00003960048,0.001958912,0.00003942498,0.00005046182,0.000006255844,0.0002562631,0.00009388207,0.002596351,0.00005491061,0.9933931,0.001497534],"study_design_scores_gemma":[0.0001094946,0.00001487328,0.001579849,0.00001240078,0.00007214483,7.952849e-7,0.00002067339,0.01119465,0.0004816867,0.0004710737,0.9859579,0.00008446265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.533364,0.02440524,0.02314213,0.1288966,0.0991343,0.009523141,0.1624435,0.001284098,0.017807],"genre_scores_gemma":[0.9585646,0.00005164912,0.0004654951,0.0001731213,0.0001072721,0.00006015672,0.01822953,0.00002005709,0.02232809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4252006,"threshold_uncertainty_score":0.9999558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0376020613180141,"score_gpt":0.2551602666443248,"score_spread":0.2175582053263107,"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."}}