{"id":"W2327315414","doi":"10.2514/6.2011-6054","title":"EVALUATION METHOD OF THRUST OSCILLATIONS IN LARGE SRM - APPLICATION TO SEGMENTED SRM's","year":2011,"lang":"en","type":"article","venue":"","topic":"Rocket and propulsion systems research","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"Office National d'études et de Recherches Aérospatiales; Centre National d’Etudes Spatiales","keywords":"Thrust; Computer science; Control theory (sociology); Aerospace engineering; Control engineering; Engineering; Artificial intelligence; Control (management)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001589919,0.00006115478,0.0001075552,0.0002141351,0.00002281322,0.000005742427,0.00008750564,0.00005236229,0.0009826599],"category_scores_gemma":[0.00004711681,0.00005524715,0.00002145137,0.0004644139,0.000003178627,0.00007734314,0.00002549435,0.00006034656,0.000115789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007052531,"about_ca_system_score_gemma":0.00001863194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002036818,"about_ca_topic_score_gemma":0.000296998,"domain_scores_codex":[0.9990508,0.0001014126,0.0002439477,0.0001157732,0.0003482878,0.000139799],"domain_scores_gemma":[0.9995307,0.00003151401,0.00001922205,0.0002032947,0.0001667138,0.00004856212],"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":[0.00002578748,0.0001073264,0.01351731,0.0001164976,0.00005058356,3.894394e-7,0.005023952,0.005718824,0.1010211,0.0007335346,0.001768732,0.871916],"study_design_scores_gemma":[0.001044509,0.00007728521,0.08567508,0.00005329333,0.00002418217,0.000002176762,0.001102434,0.6754454,0.2299726,0.001007995,0.005312857,0.0002821442],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1228664,0.00007723902,0.7482666,0.00004162582,0.00009190472,0.001216231,0.00001143001,0.000105404,0.1273232],"genre_scores_gemma":[0.9858409,0.000006245707,0.01375115,0.000007619046,0.00001750956,0.0001710142,0.00001180606,0.0000112216,0.0001825716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8716338,"threshold_uncertainty_score":0.9999306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07706405874335527,"score_gpt":0.3671802428570501,"score_spread":0.2901161841136948,"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."}}