{"id":"W4399578393","doi":"10.32614/cran.package.crs","title":"crs: Categorical Regression Splines","year":2011,"lang":"en","type":"dataset","venue":"","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Regression; Multivariate adaptive regression splines; Statistics; Mathematics; Computer science; Artificial intelligence; Nonparametric regression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006678719,0.001281041,0.001359557,0.003204321,0.0006549285,0.003253054,0.002772657,0.001742659,0.08748469],"category_scores_gemma":[0.04916654,0.001075912,0.00265145,0.005216205,0.00117379,0.002944086,0.003460443,0.004505216,0.05033624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008939165,"about_ca_system_score_gemma":0.002618534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004845237,"about_ca_topic_score_gemma":0.005796398,"domain_scores_codex":[0.9939812,0.002509337,0.0004887868,0.0009444075,0.001763382,0.0003128993],"domain_scores_gemma":[0.9867125,0.006714469,0.001091685,0.002948433,0.002012946,0.0005200672],"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":[0.0003774806,0.0001103146,0.004590727,0.00144861,0.0002230304,0.0002753381,0.0005245901,0.02343895,0.002255972,0.2672319,0.3730704,0.3264526],"study_design_scores_gemma":[0.000164963,0.0001211323,0.003246912,0.0006580373,0.00008893879,0.0005501052,0.0001560579,0.09666387,0.002130379,0.3447979,0.551257,0.0001646393],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.002504042,0.001192775,0.9415877,0.001166269,0.0007035402,0.0001508357,0.01618222,0.02474534,0.01176727],"genre_scores_gemma":[0.1028812,0.00328585,0.7919199,0.001715561,0.001146619,0.001701638,0.03875676,0.02690031,0.03169207],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.08748469,"threshold_uncertainty_score":0.2926654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2773712994377125,"score_gpt":0.4878550673423645,"score_spread":0.210483767904652,"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."}}