{"id":"W4313259365","doi":"10.3390/su15010278","title":"Patent Data Analytics for Technology Forecasting of the Railway Main Transformer","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Agency for Infrastructure Technology Advancement","keywords":"Patent analysis; Train; Technology roadmap; Engineering; Technology development; Transformer; Big data; Computer science; Transport engineering; Manufacturing engineering; Data science; Electrical engineering; Business; Data mining; Marketing; Geography","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0006839484,0.000671075,0.0004669489,0.008295614,0.0003453887,0.001178732,0.0005218514,0.000669412,0.001383028],"category_scores_gemma":[0.00425346,0.0001364708,0.0006779933,0.008327375,0.0001464827,0.001917756,0.0003908076,0.0005137175,0.0007376592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148345,"about_ca_system_score_gemma":0.0008191827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01761593,"about_ca_topic_score_gemma":0.01225573,"domain_scores_codex":[0.9994349,0.00006047788,0.00007284792,0.0001635283,0.0001983097,0.0000699837],"domain_scores_gemma":[0.9981857,0.000742004,0.0003980477,0.0001721496,0.0004251431,0.00007694923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002943887,0.0003728101,0.2403877,0.0005318743,0.0002392531,0.0009002121,0.0002862824,0.2339554,0.00806486,0.01255371,0.02419978,0.4782138],"study_design_scores_gemma":[0.00001367551,0.00007663242,0.05793502,0.00004852812,0.00007314143,0.0001225468,0.0002513251,0.9174325,0.004194495,0.00805208,0.01177325,0.00002675302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7852586,0.004281535,0.1131338,0.002373747,0.0002492352,0.0002677897,0.07244318,0.004851661,0.0171406],"genre_scores_gemma":[0.9530854,0.001124687,0.02432653,0.00004487319,0.00008492897,0.0001001083,0.02006702,0.00003179871,0.001134603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01761593,"threshold_uncertainty_score":0.03502673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2324721621652737,"score_gpt":0.2582129889161209,"score_spread":0.02574082675084724,"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."}}