{"id":"W4380569868","doi":"10.5194/egusphere-2023-986-rc1","title":"Comment on egusphere-2023-986","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Deutsches Klimarechenzentrum","keywords":"Proxy (statistics); Climatology; Climate model; Ice core; Computer science; Robustness (evolution); Error bar; Environmental science; Climate change; Algorithm; Geology; Econometrics; Statistics; Mathematics; Machine learning","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002025374,0.0009678832,0.0008937478,0.001143267,0.00256564,0.00367198,0.002286205,0.02016503,0.3521884],"category_scores_gemma":[0.01102662,0.0004723859,0.001410138,0.001355622,0.001311812,0.002700349,0.002138037,0.01011021,0.2438196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002491027,"about_ca_system_score_gemma":0.002426122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02421621,"about_ca_topic_score_gemma":0.03060985,"domain_scores_codex":[0.9985926,0.0001125217,0.0001184316,0.0001505765,0.0007301327,0.0002958277],"domain_scores_gemma":[0.9965552,0.0008814976,0.0001762165,0.0003057886,0.001471685,0.000609625],"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":[0.00002212185,0.000008040015,0.00004233986,0.00002479668,0.000001165759,0.00004270865,0.000005429833,0.000008854305,0.00004287875,0.000420539,0.9969735,0.002407529],"study_design_scores_gemma":[0.00002186643,0.00001011274,0.0003700988,0.00006163609,0.000001755442,0.00001369868,0.00002374108,0.00003038741,0.00008344995,0.000422406,0.9989522,0.000008667204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0007774775,0.001232632,0.0008634537,0.3611149,0.3047871,0.0006836274,0.01298958,0.004025323,0.3135259],"genre_scores_gemma":[0.003436804,0.0007868794,0.0004827264,0.4391067,0.04151015,0.0004594749,0.00344717,0.001148973,0.5096211],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.3521884,"threshold_uncertainty_score":0.9240246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06617597711871727,"score_gpt":0.3201765639289468,"score_spread":0.2540005868102296,"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."}}