{"id":"W4409787560","doi":"10.61091/jcmcc127a-422","title":"Random forest algorithm and multi-source data fusion based on the converter valve electrical characteristics of time-varying law extraction method and condition monitoring technology research","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Southern Power Grid","keywords":"Extraction (chemistry); Fusion; Sensor fusion; Random forest; Computer science; Algorithm; Data mining; Artificial intelligence; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001393403,0.001152589,0.001183147,0.001626495,0.0006846139,0.0008635803,0.000948346,0.0008935874,0.001135142],"category_scores_gemma":[0.003029371,0.0003538874,0.001455592,0.001536343,0.0004163103,0.001829678,0.0005967083,0.00108694,0.0003830823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006445733,"about_ca_system_score_gemma":0.001026245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007755596,"about_ca_topic_score_gemma":0.005414167,"domain_scores_codex":[0.9990354,0.0001822022,0.00006882743,0.000332607,0.0002824679,0.00009852064],"domain_scores_gemma":[0.9991987,0.0002969992,0.00009719725,0.00009862483,0.0002797761,0.00002861114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002033336,0.0001325415,0.003301431,0.0001445974,0.0001596247,0.0001685983,0.000112958,0.5273564,0.008248151,0.006253249,0.002571076,0.4513481],"study_design_scores_gemma":[0.000009677289,0.00002435644,0.0006017086,0.000005914186,0.00001584277,0.00004305111,0.00001035479,0.9948851,0.001761224,0.002156047,0.0004754939,0.00001112625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01254663,0.0002179584,0.9855275,0.00007813938,0.00004805115,0.00004474194,0.00008013847,0.000696847,0.0007599683],"genre_scores_gemma":[0.5860461,0.0004492486,0.4100706,0.000125618,0.00009679419,0.0002434069,0.0007805188,0.0001266979,0.00206094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007755596,"threshold_uncertainty_score":0.01542091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291396474118464,"score_gpt":0.3186705779421982,"score_spread":0.2957566132010135,"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."}}