{"id":"W3014413384","doi":"10.5194/essd-12-1835-2020","title":"A Canadian River Ice Database from the National Hydrometric Program Archives","year":2020,"lang":"en","type":"article","venue":"Earth system science data","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Database; Hydrology (agriculture); Climate change; Climatology; Physical geography; Geography; Geology; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008036195,0.0001262008,0.000125738,0.0001222778,0.000824013,0.0002874888,0.002645045,0.00002393459,0.0004244345],"category_scores_gemma":[0.0005436742,0.00008309817,0.00002406765,0.00163527,0.0005995902,0.0009846932,0.0001829583,0.0001854289,0.0008574848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001121442,"about_ca_system_score_gemma":0.001120043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1843854,"about_ca_topic_score_gemma":0.07927231,"domain_scores_codex":[0.9977239,0.00008602611,0.0002052662,0.0006215316,0.0008953711,0.0004679381],"domain_scores_gemma":[0.9983848,0.0003607517,0.00008280806,0.0005632207,0.00004833769,0.0005601114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004599187,0.00004126633,0.8108782,0.00009272859,0.00007272649,0.0001342878,0.004530581,0.00105811,0.00008156525,0.001231053,0.009528582,0.1723049],"study_design_scores_gemma":[0.0001257028,0.00004446575,0.3291403,0.00003577502,0.00001397038,0.00002355806,0.001000045,0.6293325,0.000002737689,0.0000284455,0.04009412,0.0001583423],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8177727,0.000633648,0.003284447,0.02175956,0.002075485,0.00265948,0.09413429,0.0005417878,0.05713857],"genre_scores_gemma":[0.9885344,0.00001226418,0.006607034,0.001536624,0.0003050875,0.000001362824,0.002965342,0.00000273327,0.00003517964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6282744,"threshold_uncertainty_score":0.9999205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04693822344842635,"score_gpt":0.2453248836255471,"score_spread":0.1983866601771207,"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."}}