{"id":"W1922643933","doi":"10.21500/20112084.841","title":"Gaining confidence with intervals: practical guidelines, advices and tricks of the trade to face real-life situations.","year":2010,"lang":"en","type":"article","venue":"International journal of psychological research","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Confidence interval; Face (sociological concept); Bayesian probability; Interpretation (philosophy); Credible interval; Statistics; Psychology; Computer science; Artificial intelligence; Mathematics; Social science; Sociology","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01647591,0.00008154033,0.0002002146,0.00038278,0.000100103,0.0002538896,0.001366751,0.00007521956,0.0009409584],"category_scores_gemma":[0.0317819,0.00003838358,0.00007603608,0.0006275655,0.0003216851,0.0003918795,0.0001957309,0.0008455487,0.00002465469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002887077,"about_ca_system_score_gemma":0.0003843998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001706809,"about_ca_topic_score_gemma":0.00006205469,"domain_scores_codex":[0.9930441,0.0005786341,0.001021283,0.0002357016,0.00494922,0.0001710211],"domain_scores_gemma":[0.9908563,0.003923513,0.0005315708,0.0002779358,0.004189209,0.0002214165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004458813,0.001446917,0.1418577,0.00001069639,0.0002662347,0.000122175,0.006329423,0.001641944,0.04835896,0.08947121,0.08296769,0.6230682],"study_design_scores_gemma":[0.002076464,0.002052533,0.8998703,0.0002483166,0.0000184826,0.000623268,0.008169146,0.00456088,0.001416973,0.02688776,0.05388542,0.0001904636],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8503476,0.0000347386,0.01664112,0.1272428,0.0005881896,0.0001620555,0.000005460148,0.000003349815,0.004974716],"genre_scores_gemma":[0.9895802,0.0001223778,0.008637781,0.001152317,0.0002233103,0.000005640496,3.425602e-7,0.000003904009,0.000274184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7580126,"threshold_uncertainty_score":0.9999723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.718899948039882,"score_gpt":0.7159798591573202,"score_spread":0.002920088882561789,"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."}}