{"id":"W1975174365","doi":"10.1175/jas3437.1","title":"A Simple Parameterization Coupling the Convective Daytime Boundary Layer and Fair-Weather Cumuli","year":2005,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Pacific Northwest National Laboratory; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Battelle; University of Wyoming; Laboratory Directed Research and Development; U.S. Department of Energy; National Science Foundation","keywords":"Convective Boundary Layer; Cloud cover; Boundary layer; Environmental science; Daytime; Cloud base; Meteorology; Planetary boundary layer; Atmospheric sciences; Cloud top; Potential temperature; Liquid water content; Cloud computing; Geology; Mechanics; Physics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008346809,0.0001262738,0.0001570875,3.941705e-7,0.0007425358,0.0001591162,0.0006406987,0.00003961122,0.0007557092],"category_scores_gemma":[0.00009279271,0.00005923069,0.00009088885,0.0004851783,0.001145758,0.0004708557,0.000226066,0.0001677469,0.00002584556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009670595,"about_ca_system_score_gemma":0.00005184255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009183391,"about_ca_topic_score_gemma":0.0000282495,"domain_scores_codex":[0.9986774,0.00006308043,0.0002924817,0.0001794015,0.0005522669,0.0002354073],"domain_scores_gemma":[0.9992028,0.0001329389,0.0004002332,0.0001700818,0.00002118417,0.000072712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007551705,0.0001763831,0.4999702,0.000005967768,0.00008777066,0.000006626393,0.00539475,0.4291933,0.02294032,0.0002163219,0.008186523,0.03374636],"study_design_scores_gemma":[0.0007439429,0.0005554462,0.2451942,0.00004918585,0.0001174409,0.000254205,0.003949858,0.6827508,0.002391375,0.00338321,0.06019429,0.0004159938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938986,0.0004277852,0.001289092,0.002722359,0.0002133988,0.0001210162,5.353691e-7,0.000007996709,0.001319271],"genre_scores_gemma":[0.9918164,0.0000632024,0.006364435,0.001103372,0.0001286341,0.000002403407,4.679463e-8,0.000008111478,0.0005133832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2547759,"threshold_uncertainty_score":0.8274486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271988618665112,"score_gpt":0.2448002933203394,"score_spread":0.2320804071336883,"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."}}