{"id":"W2734766137","doi":"","title":"Sequential determination of sample size for robust linear regression: application to microarray experimental design","year":2006,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Linear regression; Sample size determination; Computer science; Sample (material); Regression analysis; Statistics; Artificial intelligence; Mathematics; Machine learning; Chromatography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014883,0.0003543839,0.0004813443,0.0001964666,0.0002392267,0.0001183464,0.0009328204,0.0003953594,0.0003361658],"category_scores_gemma":[0.001893725,0.000380623,0.0002855512,0.0004043414,0.0001349033,0.00007513035,0.0005236691,0.0003026128,0.000006861424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002659998,"about_ca_system_score_gemma":0.0001941622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129367,"about_ca_topic_score_gemma":0.0001456943,"domain_scores_codex":[0.997313,0.000538735,0.0006684994,0.0008051425,0.0003673374,0.0003073017],"domain_scores_gemma":[0.9937361,0.00252818,0.0007520954,0.001447284,0.001402213,0.0001341209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001080218,0.001163004,0.0006310896,0.0005457055,0.0001042255,9.256557e-7,0.002234309,0.001037593,0.9771488,0.001100146,0.001725355,0.01420087],"study_design_scores_gemma":[0.0004506758,8.666081e-7,0.00005715148,0.0004039875,0.00012026,0.000002264409,0.0001028922,0.02457126,0.9718251,0.0007268493,0.00140425,0.0003345025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03720096,0.0005843766,0.9565619,0.0007639438,0.00007053354,0.0005439523,0.0002557018,0.0001358353,0.003882797],"genre_scores_gemma":[0.4102875,0.00003720454,0.5847524,0.00002880131,0.00006682487,0.00035478,0.0008374752,0.00005236085,0.003582714],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3730865,"threshold_uncertainty_score":0.9998646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02945021104008218,"score_gpt":0.2885029463104181,"score_spread":0.259052735270336,"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."}}