{"id":"W4306836480","doi":"10.5194/hess-2022-334-rc1","title":"Comment on hess-2022-334","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of Saskatchewan","funders":"","keywords":"Predictability; Downscaling; Numerical weather prediction; Computer science; Data assimilation; Merge (version control); Forcing (mathematics); Meteorology; Climate model; Climatology; Environmental science; Forecast skill; Machine learning; Climate change; Geography; Precipitation; Mathematics; Geology","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":[0.001328165,0.0008574524,0.0009057701,0.001116239,0.001254899,0.004277971,0.001684158,0.008682964,0.1763831],"category_scores_gemma":[0.00665125,0.0004212469,0.001196316,0.0021699,0.0008481122,0.002781126,0.001452169,0.006356299,0.1076832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002494177,"about_ca_system_score_gemma":0.002826211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03779798,"about_ca_topic_score_gemma":0.0329686,"domain_scores_codex":[0.9988899,0.0001048093,0.00008073293,0.0001288798,0.000619908,0.000175863],"domain_scores_gemma":[0.9978681,0.0005216269,0.0001144637,0.0002383351,0.001107034,0.0001505332],"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":[0.00002441865,0.000002801521,0.00004103841,0.00005094427,0.000002010016,0.00002819238,0.000005356557,0.00004426605,0.00006516903,0.0009444004,0.9969898,0.001801546],"study_design_scores_gemma":[0.00001740084,0.000005554546,0.0008102159,0.00006511377,0.000003391413,0.00001299763,0.00002806982,0.0000879105,0.0001862896,0.0009847918,0.9977844,0.00001381129],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.001373722,0.002632723,0.001695551,0.2736108,0.2112322,0.0004907167,0.1227984,0.004550501,0.3816154],"genre_scores_gemma":[0.01633629,0.002517032,0.001576685,0.2802797,0.03458294,0.0008069905,0.03633766,0.002219131,0.6253437],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.1763831,"threshold_uncertainty_score":0.5900603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07054928221332156,"score_gpt":0.2892614039716752,"score_spread":0.2187121217583536,"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."}}