{"id":"W4398457384","doi":"10.7910/dvn/kg1mh6","title":"Data support for \"Hygroscopic seeding effects of giant aerosol particles simulated by the Lagrangian-particle-based direct numerical simulation\"","year":2021,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Aeolian processes and effects","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Seeding; Lagrangian; Aerosol; Particle (ecology); Mechanics; Environmental science; Meteorology; Materials science; Statistical physics; Physics; Mathematics; Applied mathematics; Thermodynamics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005007003,0.0003514479,0.0005532748,0.00004264629,0.0002579679,0.0001635022,0.001121461,0.0002041595,0.004268667],"category_scores_gemma":[0.001256748,0.0002538332,0.0001204057,0.0003760282,0.0001502049,0.0003979059,0.0001428076,0.0002335394,0.001978104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008759532,"about_ca_system_score_gemma":0.0002217445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009431292,"about_ca_topic_score_gemma":0.0003844987,"domain_scores_codex":[0.997626,0.0001849983,0.0004756292,0.0006984942,0.0004678947,0.0005469904],"domain_scores_gemma":[0.995149,0.002715984,0.0003192554,0.001512887,0.000098078,0.0002047779],"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":[0.0001417841,0.00007883277,0.0003355,0.001028082,0.0001080376,0.00004230005,0.00001519489,0.04992002,0.0001390797,3.221373e-7,0.9470905,0.001100344],"study_design_scores_gemma":[0.0006128746,0.0001504391,0.0001558563,0.0001212813,0.0002777596,0.000001608044,0.00001608817,0.3847588,0.001077587,0.000001666966,0.6126221,0.0002039192],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001760311,0.00005800503,0.001212931,0.00002476759,0.0004922314,0.0007569063,0.9956467,0.00003958202,0.00000857526],"genre_scores_gemma":[0.08524197,0.00005341524,0.0002406927,0.0004107573,0.0001346693,0.000006369758,0.91386,0.00001285445,0.00003932809],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3348388,"threshold_uncertainty_score":0.9999914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226403658565496,"score_gpt":0.2617134067534872,"score_spread":0.2390730408969376,"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."}}