{"id":"W2065973802","doi":"10.1016/j.quascirev.2004.05.010","title":"How to combine sparse proxy data and coupled climate models","year":2004,"lang":"en","type":"article","venue":"Quaternary Science Reviews","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Universität Bremen; Deutsche Forschungsgemeinschaft; University of Victoria","keywords":"Proxy (statistics); Sea surface temperature; Glacial period; Last Glacial Maximum; Earth system science; Isotopes of oxygen; Foraminifera; Environmental science; Geology; Climate model; Climatology; Oceanography; Climate change; Computer science; Paleontology; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001111013,0.0002177658,0.0002839014,0.0000115196,0.0002735609,0.0001481925,0.001269137,0.00003219538,0.00006608298],"category_scores_gemma":[0.00003862424,0.0001653956,0.0000286721,0.0004378791,0.0007897122,0.001726149,0.001897903,0.00009679064,0.0004171003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002242695,"about_ca_system_score_gemma":0.00001530565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002729391,"about_ca_topic_score_gemma":0.00002281718,"domain_scores_codex":[0.9978281,0.00002843056,0.0002782968,0.0008400776,0.0004844084,0.0005407341],"domain_scores_gemma":[0.9984973,0.000007823061,0.0001183968,0.001008499,0.000002940036,0.0003650185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001137923,0.001059893,0.265223,0.0003211181,0.00001798525,0.0001876321,0.003351438,0.3160329,0.07846797,0.002877309,0.0009849438,0.331362],"study_design_scores_gemma":[0.0007954871,0.0003988686,0.01150886,0.0001996791,0.00003798402,0.000116794,0.0002154673,0.93407,0.0001920854,0.002755142,0.04887585,0.0008337591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9262752,0.0009946063,0.06827643,0.002243479,0.0001534146,0.001126438,0.000008806292,0.0000476216,0.0008740053],"genre_scores_gemma":[0.8510709,0.0116157,0.1351987,0.001267118,0.00003195006,0.00004410062,0.00001140717,0.00002416064,0.0007358811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6180372,"threshold_uncertainty_score":0.6744636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04081433038118511,"score_gpt":0.2668098445672609,"score_spread":0.2259955141860757,"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."}}