{"id":"W4292399143","doi":"10.1051/0004-6361:20053065","title":"Rotational mixing in low-mass stars","year":2006,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Stellar, planetary, and galactic studies","field":"Physics and Astronomy","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds De La Recherche Scientifique - FNRS; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Stars; Metallicity; Mixing (physics); Low Mass; Astrophysics; Rotation (mathematics); Physics; Astronomy; Computer science; Artificial intelligence","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":[0.00004862036,0.0001992094,0.0002183824,0.00005098122,0.0001316534,0.00005302066,0.0000777438,0.00002237471,0.0001006437],"category_scores_gemma":[0.00000107687,0.0002044277,0.00006030955,0.0001054202,0.00006821306,0.0001708668,0.00003312896,0.0001558105,0.00002895417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001617759,"about_ca_system_score_gemma":0.00003272085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004665864,"about_ca_topic_score_gemma":0.00001044003,"domain_scores_codex":[0.9990264,0.00002621189,0.0002315014,0.0002644746,0.0001224553,0.0003289706],"domain_scores_gemma":[0.9996383,0.00008215722,0.00008654051,0.0001159205,0.0000273619,0.00004974198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001592698,0.00007839212,0.9463691,0.000006292277,0.00003640956,0.000001890983,0.00006496715,0.002338161,0.0000911285,0.005158909,0.0001301059,0.04570868],"study_design_scores_gemma":[0.001059879,0.00005155937,0.9868018,0.00004059285,0.00003173017,5.750861e-7,0.0003854687,0.0004383083,0.0005355172,0.004641344,0.005705068,0.0003081593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8818021,0.0001070952,0.09355795,0.00008842777,0.0001519636,0.0001748434,0.00003207077,0.00002277919,0.0240627],"genre_scores_gemma":[0.9825332,0.000001379402,0.01651172,0.00001360366,0.0005848894,0.00001638035,0.000164699,0.00001379098,0.0001603128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1007311,"threshold_uncertainty_score":0.8336322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005257205039332444,"score_gpt":0.1915842000797242,"score_spread":0.1863269950403918,"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."}}