{"id":"W4360937528","doi":"10.22541/essoar.167979641.11160267/v1","title":"Diagnosing the radiation biases in global climate models using radiative kernels","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Earth's energy budget; Environmental science; Radiative transfer; Radiation; Climate model; Troposphere; Atmosphere (unit); GCM transcription factors; Cloud cover; Climatology; Humidity; Meteorology; Atmospheric sciences; General Circulation Model; Cloud computing; Climate change; Physics; Computer science; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001890662,0.0007400831,0.0004652687,0.0006634292,0.0002725745,0.001102848,0.0005789367,0.0008367203,0.0003085068],"category_scores_gemma":[0.007691613,0.0004132692,0.0007920857,0.0005975284,0.0004794955,0.001084099,0.0009358141,0.0006185049,0.00007826097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008230398,"about_ca_system_score_gemma":0.0006632399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01692595,"about_ca_topic_score_gemma":0.00642385,"domain_scores_codex":[0.9995254,0.0001871013,0.00004657791,0.00009037039,0.00008987756,0.0000606426],"domain_scores_gemma":[0.9981979,0.0009771778,0.0002825882,0.0002887769,0.0001918385,0.00006181045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007533634,0.00004749153,0.04801047,0.0000325602,0.0001399414,0.00004352057,0.00007438959,0.9369505,0.004603882,0.001215005,0.0001521645,0.00865477],"study_design_scores_gemma":[0.00001408054,0.00001203649,0.009115052,0.000006941687,0.00002284277,0.000009923083,0.00001364012,0.9877676,0.001959503,0.0008846457,0.0001808245,0.0000129355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718199,0.0002293442,0.02622482,0.0001020317,0.00002822671,0.00002108188,0.0002514939,0.0005029908,0.0008199941],"genre_scores_gemma":[0.9929206,0.00006106847,0.006655722,0.00001454736,0.000008741854,0.000009118343,0.000190791,0.00005713053,0.00008230092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01692595,"threshold_uncertainty_score":0.03365487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1359958480393061,"score_gpt":0.3348652645043889,"score_spread":0.1988694164650828,"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."}}