{"id":"W7064940992","doi":"","title":"Data Reduction pipeline for MOST Guide Stars and Application to two Observing Runs. VerÃ¶ffentlichungen der Kommission fÃ¼r Astronomie|Communications in Asteroseismology|Communications in Asteroseismology 156 156|","year":2008,"lang":"en","type":"other","venue":"Oesterreichisches Musiklexikon online (Institut für kunst- und musikhistorische Forschungen der  Österreichischen Akademie der Wissenschaften)","topic":"Electrical and Electromagnetic Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Verkehr, Innovation und Technologie; Austrian Science Fund; Canadian Space Agency; National Science Foundation","keywords":"Asteroseismology; Pipeline (software); Data reduction; Reduction (mathematics); Stars","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.001492052,0.002052672,0.002506012,0.002303883,0.0008486097,0.0002569768,0.005803926,0.001316873,0.0001577617],"category_scores_gemma":[0.0002802798,0.002187232,0.0003901992,0.001911991,0.0009698329,0.001104154,0.004376963,0.0033556,0.0001260145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008999431,"about_ca_system_score_gemma":0.001365366,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01244911,"about_ca_topic_score_gemma":0.01583257,"domain_scores_codex":[0.9888325,0.001031351,0.003240437,0.003548017,0.0008976899,0.002449968],"domain_scores_gemma":[0.9888149,0.0005287998,0.001440346,0.007801094,0.0003458892,0.001068953],"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":[0.002632455,0.00953661,0.04030091,0.001727285,0.004906667,0.0001273693,0.007238056,0.002181865,0.04701902,0.006575529,0.5362835,0.3414707],"study_design_scores_gemma":[0.004727352,0.0004300619,0.001720174,0.0008547978,0.0009000482,0.0001080663,0.0004179362,0.02449499,0.0002377483,0.0002951155,0.9635473,0.002266366],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1491624,0.2037052,0.3014766,0.05944246,0.008848403,0.05747192,0.01309715,0.002855342,0.2039405],"genre_scores_gemma":[0.25982,0.006005581,0.3651372,0.003546915,0.007545576,0.00760225,0.05483654,0.003910006,0.291596],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4272639,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06158755606283366,"score_gpt":0.3823156021290534,"score_spread":0.3207280460662197,"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."}}