{"id":"W2787744881","doi":"10.1109/ieem.2017.8290163","title":"A random forest method for obsolescence forecasting","year":2017,"lang":"en","type":"article","venue":"","topic":"Transportation Systems and Infrastructure","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Obsolescence; Computer science; Random forest; Reliability engineering; Artificial intelligence; Business; Engineering; Marketing","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":[],"consensus_categories":[],"category_scores_codex":[0.0003182754,0.0001095467,0.0001708842,0.00005282535,0.0005966594,0.000610981,0.0002711918,0.0000478735,0.0001118661],"category_scores_gemma":[0.0001788207,0.00008355913,0.00009843172,0.00003670044,0.00002330571,0.001157978,0.0000270303,0.00004605437,0.00002271598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004505707,"about_ca_system_score_gemma":0.000008645075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006348884,"about_ca_topic_score_gemma":0.0007743608,"domain_scores_codex":[0.9993047,0.000001842106,0.0002116666,0.0001886867,0.0001071246,0.0001859696],"domain_scores_gemma":[0.9992388,0.00003918516,0.0002948346,0.0002660234,0.0001526732,0.000008418944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003845242,0.00003620566,0.4844355,0.001013949,0.00007677742,0.00001059974,0.0001339536,0.0008214773,0.001058793,0.3334118,0.0235987,0.1550178],"study_design_scores_gemma":[0.006667084,0.00001253366,0.3710252,0.0001761805,0.00009601886,0.000005002683,0.0003387421,0.2254787,0.0001438714,0.01782308,0.3776981,0.0005354538],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03909519,0.00001762323,0.8960495,0.0008884153,0.0009070581,0.0007344601,0.000006317689,0.0001383833,0.06216306],"genre_scores_gemma":[0.9648725,4.256103e-7,0.03216949,0.0007004255,0.001199278,0.00004729697,0.00001452001,0.00001800728,0.0009780865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9257773,"threshold_uncertainty_score":0.5891703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04372885204444823,"score_gpt":0.2779863087640373,"score_spread":0.2342574567195891,"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."}}