{"id":"W639689800","doi":"","title":"Classification Algorithm for Characterizing Long Multiple Trailer Truck Movements","year":2007,"lang":"en","type":"article","venue":"Transportation Research Board 86th Annual MeetingTransportation Research Board","topic":"Transport Systems and Technology","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Trailer; Transport engineering; Service (business); Level of service; Computer science; Operations research; Engineering; Business; Automotive engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007597427,0.0005997857,0.0007277292,0.002177237,0.0009658679,0.0001663694,0.0008459279,0.0007715074,0.0001645109],"category_scores_gemma":[0.0001558835,0.0006598776,0.0003496831,0.002346279,0.0005876346,0.0009578657,0.000008002598,0.001931777,0.000101745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003971515,"about_ca_system_score_gemma":0.0002035821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001690124,"about_ca_topic_score_gemma":0.01523416,"domain_scores_codex":[0.990724,0.0002462067,0.001991212,0.001227578,0.003028037,0.002782968],"domain_scores_gemma":[0.9943051,0.001244112,0.0001975765,0.0007427701,0.002755091,0.0007553825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002010756,0.001211279,0.472251,0.003949459,0.0009618719,0.000594444,0.01937864,0.001919137,0.1504408,0.009018887,0.007351516,0.3309122],"study_design_scores_gemma":[0.003077632,0.0004711913,0.9179922,0.0002934502,0.0000377038,0.000001121495,0.007068166,0.005013106,0.01935842,0.000505801,0.04539337,0.0007878475],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8605801,0.0002974258,0.1307161,0.0003567288,0.0006761395,0.003992417,0.001304009,0.001216214,0.0008608348],"genre_scores_gemma":[0.9838717,0.0002999253,0.01105647,0.00004887185,0.000452842,0.00136439,0.001949087,0.0002569104,0.00069979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4457412,"threshold_uncertainty_score":0.9995853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05844390495900568,"score_gpt":0.3481920410008452,"score_spread":0.2897481360418395,"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."}}