{"id":"W4231346947","doi":"10.5194/essd-2021-51-rc2","title":"Comment on essd-2021-51","year":2021,"lang":"en","type":"peer-review","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eesti Teadusagentuur; H2020 Marie Skłodowska-Curie Actions; Sihtasutus Archimedes","keywords":"Metadata; Flagging; Scale (ratio); Computer science; Outlier; Data quality; Quality (philosophy); Download; Replicate; Data mining; Database; Environmental science; Information retrieval; Cartography; Geography; Statistics; World Wide Web; Mathematics; Metric (unit); Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005385311,0.0003741109,0.000582093,0.0000220121,0.0001222767,0.00004567771,0.0005349888,0.0002734267,0.2003589],"category_scores_gemma":[0.0003128327,0.0002771298,0.0002199024,0.0002675419,0.0001522008,0.00002844783,0.0007430643,0.0006594693,0.01263771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003713349,"about_ca_system_score_gemma":0.00002001567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000473088,"about_ca_topic_score_gemma":0.00008176587,"domain_scores_codex":[0.9973964,0.0001778916,0.0003725048,0.0007789903,0.0008322239,0.0004419786],"domain_scores_gemma":[0.9987466,0.0001572283,0.0001441432,0.0007624855,0.0000124708,0.0001770656],"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":[0.000001381856,0.0001105625,0.0000161413,0.0001278001,0.00001256712,0.00004977456,0.000005366029,0.0003515004,0.000007069183,0.00004823238,0.9834501,0.01581957],"study_design_scores_gemma":[0.00007612282,0.0001046848,0.00002421567,0.001340885,0.00004705217,0.000008720376,7.631622e-7,0.0002160943,0.0000373013,0.0001339541,0.9976783,0.0003319455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001151659,0.001759563,0.00005802483,0.4436167,0.001651678,0.0003469459,0.00005328371,0.0000895013,0.5523092],"genre_scores_gemma":[0.0003104826,0.002178334,0.002065436,0.3215935,0.0003137714,0.00005912197,0.0007443469,0.00005451167,0.6726805],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1877212,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05010223786007216,"score_gpt":0.3015817221813534,"score_spread":0.2514794843212812,"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."}}