{"id":"W4393797289","doi":"10.5281/zenodo.1318352","title":"R-Code For Publication: Ensembles Of Ensembles: Combining The Predictions From Multiple Machine Learning Methods","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Analysis with R","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Mount Allison University","funders":"","keywords":"Computer science; Code (set theory); Artificial intelligence; Machine learning; Programming language","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","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002758931,0.0002946912,0.0004138829,0.0004866236,0.00315867,0.001710089,0.005889425,0.0001728943,0.002237056],"category_scores_gemma":[0.006309385,0.0002545386,0.0001655722,0.001226548,0.0002963729,0.000594209,0.003814597,0.0006058089,0.001017457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001171445,"about_ca_system_score_gemma":0.00002162861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002305822,"about_ca_topic_score_gemma":0.00001625409,"domain_scores_codex":[0.9958692,0.001484573,0.0006840993,0.0008996282,0.0006258694,0.0004366059],"domain_scores_gemma":[0.9944688,0.0006931395,0.0007449014,0.00221346,0.001709517,0.0001701636],"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":[0.0000236145,0.0001015994,0.000001167139,0.00005720758,0.0002073275,0.000001065296,0.0004116007,0.00008754878,0.0002962507,0.0005286821,0.9836541,0.01462978],"study_design_scores_gemma":[0.0004413108,0.0002197164,0.00002514873,0.00005821339,0.0001104329,0.00001983843,0.0000836567,0.0401169,0.0002620921,0.0002043208,0.9582321,0.0002262699],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000008390726,0.0001228042,0.3827286,0.0006763648,0.0001764448,0.0004581506,0.614806,0.0003922951,0.000630971],"genre_scores_gemma":[0.0003760073,0.000188553,0.026929,0.0001590445,0.0002664394,7.042577e-7,0.9712268,0.0005511999,0.0003023133],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3564208,"threshold_uncertainty_score":0.9999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05771401729794948,"score_gpt":0.3067166448796939,"score_spread":0.2490026275817444,"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."}}