{"id":"W4247369394","doi":"10.18356/1768c00d-en","title":"Acknowledgements","year":2016,"lang":"en","type":"book-chapter","venue":"Statistical papers. Series M","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Commission; Official statistics; European commission; Work (physics); Business cycle; Statistical analysis; Summary statistics; Order (exchange); Statistics; Political science; Economics; Geography; Finance; Economic policy; Engineering; Macroeconomics; European union; Mathematics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004951507,0.0004492152,0.0005221362,0.0001412414,0.0006581261,0.0001454218,0.0005530328,0.0003698003,0.07600892],"category_scores_gemma":[0.0004123962,0.0003947771,0.0001943128,0.00006017647,0.002107567,0.0001984739,0.0002082351,0.0003242975,0.01077133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002410387,"about_ca_system_score_gemma":0.0001775784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000381727,"about_ca_topic_score_gemma":0.002563309,"domain_scores_codex":[0.9969202,0.0001125459,0.0004937304,0.0006841802,0.001072338,0.0007170129],"domain_scores_gemma":[0.9983914,0.0002421573,0.0002075724,0.0005081027,0.0003449239,0.0003058524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003303459,0.00001954478,0.0001753646,0.00006903565,0.0001716879,0.0000662707,0.0003988344,4.019881e-8,0.00000162609,0.8878435,0.08019511,0.03102591],"study_design_scores_gemma":[0.000167656,0.00006269437,0.001146375,0.0000931775,0.0001003069,2.969617e-7,0.0001540898,1.207342e-7,5.711387e-7,0.210339,0.7875066,0.0004290397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000005271352,0.0002179183,0.0003967538,0.0007487786,0.002527134,0.0006226129,0.0008888954,0.0002048537,0.9943878],"genre_scores_gemma":[0.002128408,0.001947069,0.001145938,0.000422953,0.001333197,0.00004082034,0.00008673976,0.00008665065,0.9928082],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7073115,"threshold_uncertainty_score":0.9998504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864022280903064,"score_gpt":0.2892728031145916,"score_spread":0.270632580305561,"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."}}