{"id":"W2073673725","doi":"10.3844/ajeassp.2011.363.371","title":"Application of Multiple Imputations to Freight Transportation Survey Data: A Case Study of Commodity Flow Survey","year":2011,"lang":"en","type":"article","venue":"American Journal of Engineering and Applied Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Mongkut's University of Technology North Bangkok","keywords":"Survey data collection; Commodity; Data quality; Scope (computer science); Transport engineering; Quarter (Canadian coin); Survey methodology; Quality (philosophy); Business; Computer science; Statistics; Engineering; Marketing; Mathematics; Geography; Finance","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":[0.0922568,0.000981826,0.001539695,0.002356139,0.002306738,0.001983619,0.003728125,0.002686526,0.002126327],"category_scores_gemma":[0.185778,0.0009593525,0.00304592,0.009561404,0.001422859,0.001988989,0.002211365,0.00295576,0.0003470375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001941613,"about_ca_system_score_gemma":0.002840745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01847061,"about_ca_topic_score_gemma":0.01416278,"domain_scores_codex":[0.8758104,0.1128299,0.00283392,0.003318224,0.003956364,0.001251242],"domain_scores_gemma":[0.6949539,0.2586806,0.01572495,0.02033366,0.009270995,0.001035877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001534566,0.001729057,0.5474778,0.001279085,0.003031512,0.006911275,0.009505624,0.1943474,0.0008155636,0.02826857,0.006907717,0.1981919],"study_design_scores_gemma":[0.0003874816,0.001371812,0.1068744,0.00047106,0.0009839208,0.00264127,0.007802316,0.8360999,0.002753378,0.03296668,0.007427296,0.0002204265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5520445,0.0007044859,0.4380712,0.002613448,0.0001263519,0.001215954,0.001414314,0.0002531855,0.003556549],"genre_scores_gemma":[0.7820529,0.0002358124,0.2152227,0.0001877124,0.00005777703,0.0007154464,0.0007364945,0.00005127384,0.0007398221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0922568,"threshold_uncertainty_score":0.4879064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06487977152179507,"score_gpt":0.3027271510525511,"score_spread":0.237847379530756,"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."}}