{"id":"W4394082051","doi":"10.6084/m9.figshare.21485874","title":"Dataset - variable selection","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts","funders":"","keywords":"Selection (genetic algorithm); Variable (mathematics); Feature selection; Computer science; Artificial intelligence; Mathematics","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001191348,0.0001959818,0.0001630949,0.0001789887,0.0001569599,0.0004140533,0.001784739,0.0002332546,0.02972314],"category_scores_gemma":[0.001420312,0.0001958978,0.00003155658,0.0007315158,0.000002160675,0.0003648469,0.0007107714,0.0005117474,0.1473129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004592987,"about_ca_system_score_gemma":0.0001804124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003058605,"about_ca_topic_score_gemma":0.00006498848,"domain_scores_codex":[0.9985639,0.00008831273,0.0001894648,0.0006039835,0.0003030711,0.0002512957],"domain_scores_gemma":[0.9982265,0.0001688411,0.0002000208,0.001259078,0.00006416724,0.00008140998],"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":[5.284735e-7,0.00001400236,1.094673e-7,0.0001614033,0.000009186005,0.00000635859,0.000001098759,0.00002452208,5.821587e-7,0.00003629235,0.9990591,0.0006868268],"study_design_scores_gemma":[0.00005262474,0.00002123944,0.00004460438,0.0003455934,0.000006389265,0.00001050426,3.419614e-7,0.004430653,0.000001557986,0.00005655207,0.9948131,0.0002167892],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[9.736719e-10,0.00002603337,0.0002809887,0.0001116604,0.00022938,0.0001199201,0.9988476,0.0003034357,0.0000809794],"genre_scores_gemma":[2.182339e-8,0.000006374138,0.001239052,0.0002580325,0.0002712245,0.0001608632,0.9976292,0.00001358493,0.0004216647],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1175898,"threshold_uncertainty_score":0.9711638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04609292544505646,"score_gpt":0.2993425333155434,"score_spread":0.253249607870487,"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."}}