{"id":"W2555259251","doi":"10.20944/preprints201609.0088.v1","title":"Multi-range Conditional Random Field for Classifying Railway Electrification System Objects","year":2016,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Conditional random field; Electrification; Pairwise comparison; Computer science; Classifier (UML); Artificial intelligence; Support vector machine; Unary operation; Machine learning; Engineering; Mathematics; Electrical engineering; Electricity","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007447076,0.0003095279,0.0003626193,0.00007458117,0.0003127801,0.00003535549,0.0005057071,0.000403567,0.0005009348],"category_scores_gemma":[0.0003089891,0.0002793925,0.0002629471,0.0001055426,0.0001174015,0.00009015416,0.0003195281,0.0004235927,0.002755153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000508703,"about_ca_system_score_gemma":0.00006601069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001712159,"about_ca_topic_score_gemma":0.00003620463,"domain_scores_codex":[0.9975629,0.0001405376,0.0005063168,0.001058937,0.0003456413,0.0003856612],"domain_scores_gemma":[0.9979593,0.0003992477,0.0003946057,0.001049546,0.00005546512,0.000141862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005703391,0.0004775894,0.2058161,0.0007740618,0.0003382217,0.000007756777,0.001953159,0.002375987,0.7705599,0.001870553,0.004336839,0.01091952],"study_design_scores_gemma":[0.006959204,0.00006410461,0.5412744,0.0008906779,0.0003291594,0.00005205699,0.0002918406,0.02733777,0.3757098,0.006379927,0.03879851,0.001912532],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5445258,0.00009468563,0.3940535,0.002303674,0.001090264,0.005093591,0.0001970871,0.0007566715,0.05188475],"genre_scores_gemma":[0.9930654,0.00002458211,0.003299973,0.0001378865,0.0002545458,0.0003905759,0.0001493804,0.00004422523,0.002633421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4485396,"threshold_uncertainty_score":0.9999658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08882682094727641,"score_gpt":0.3263791956059471,"score_spread":0.2375523746586707,"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."}}