{"id":"W4285796472","doi":"10.5194/essd-2022-208","title":"The hourly wind-bias adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Manitoba Hydro","keywords":"Precipitation; Environmental science; Climate change; Snow; Meteorology; Climatology; Geography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007347427,0.0007565543,0.0005237642,0.002528443,0.00114915,0.001039226,0.001329517,0.0003948561,0.004356527],"category_scores_gemma":[0.002883493,0.000295851,0.0003968948,0.008185106,0.0003307264,0.0004505719,0.0006141529,0.0008571681,0.002983088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009614663,"about_ca_system_score_gemma":0.02024493,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.976533,"about_ca_topic_score_gemma":0.980922,"domain_scores_codex":[0.9989104,0.00004172815,0.00006791543,0.0001384741,0.0007059822,0.0001354785],"domain_scores_gemma":[0.9929788,0.0001335941,0.0002942741,0.0003253545,0.005992198,0.0002757563],"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":[0.0007987787,0.0002125471,0.1277742,0.0007096867,0.0003575241,0.0002212912,0.0003034932,0.01387629,0.002291248,0.001683905,0.8134547,0.03831633],"study_design_scores_gemma":[0.0001756624,0.0000403315,0.690502,0.0001288279,0.0000565442,0.00006995907,0.0003152947,0.008780312,0.002935593,0.0002798245,0.2966183,0.00009742969],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02193332,0.0001100587,0.0004168432,0.0001140971,0.00005672093,0.00006184138,0.9736146,0.0002808282,0.0034117],"genre_scores_gemma":[0.02722941,0.0001098731,0.001174003,0.00005433991,0.00001214584,0.00007596287,0.9682909,0.00006339652,0.002989894],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.023467,"threshold_uncertainty_score":0.06975955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.133162458178444,"score_gpt":0.2686176237546041,"score_spread":0.13545516557616,"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."}}