{"id":"W4400453381","doi":"10.3390/app14145971","title":"Parameter Design and Optimization of Grass Aerial Seeding Tower Based Computational Fluid Dynamics","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Cyclone Separators and Fluid Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nozzle; Airflow; Seeding; Tower; Computational fluid dynamics; Traverse; Marine engineering; Acceleration; Mechanics; Fluent; Environmental science; Meteorology; Engineering; Structural engineering; Aerospace engineering; Geology; Physics; Mechanical engineering; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002257097,0.00008085994,0.00009171385,0.0001008005,0.00006081432,0.000104181,0.00007183362,0.00003815857,0.00001509031],"category_scores_gemma":[0.000006228132,0.00007297606,0.00001805351,0.0003092907,0.0001295835,0.00009492495,0.00001324539,0.00004241692,0.000001303863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000255689,"about_ca_system_score_gemma":0.00003143381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002919747,"about_ca_topic_score_gemma":0.000001845601,"domain_scores_codex":[0.9994417,0.000005852766,0.000136895,0.0001477762,0.0001535367,0.0001142061],"domain_scores_gemma":[0.9997356,0.0001682222,0.00001153712,0.00004045158,0.00001382749,0.00003036513],"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":[0.00000343628,0.000003513638,0.00002885063,0.00001793329,0.000005890934,4.60917e-7,0.00005653863,0.9857456,0.002152779,0.01095076,0.00009275089,0.0009415059],"study_design_scores_gemma":[0.00007597802,0.00001995611,0.00003783466,0.00001198533,0.000007841406,0.000001326954,0.00002643877,0.9977127,0.000609841,0.001389701,0.00001860426,0.00008779344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1124942,0.00008971727,0.8862153,0.0000206011,0.0002666319,0.00009985645,0.000007170743,0.00009175172,0.0007148026],"genre_scores_gemma":[0.8637382,0.000009015922,0.1361869,0.00001326608,0.00002500318,0.000007024351,0.000009837969,0.00000782796,0.000002903004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7512441,"threshold_uncertainty_score":0.2975877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169973911997915,"score_gpt":0.2227868656544924,"score_spread":0.2110871265345133,"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."}}