{"id":"W4388506618","doi":"10.18757/ejtir.2018.18.1.3222","title":"Development of a household travel resource allocation model","year":2018,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; University of Toronto","keywords":"Heuristics; Metropolitan area; Schedule; Context (archaeology); Public transport; Duration (music); Transport engineering; Demographics; Resource allocation; Land use; Travel behavior; Computer science; Budget constraint; Heuristic; Resource (disambiguation); Business; Operations research; Geography; Economics; Engineering; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001221748,0.00008930253,0.0001458316,0.000295511,0.00008964851,0.00001070947,0.0001696486,0.00003043133,0.00003049843],"category_scores_gemma":[0.000009168039,0.00007747005,0.00003655665,0.0003014496,0.0001723682,0.0001181091,0.000004465496,0.0003478324,0.000001149142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002230607,"about_ca_system_score_gemma":0.0001013386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.336702e-7,"about_ca_topic_score_gemma":0.000006386548,"domain_scores_codex":[0.9987435,0.00003918686,0.000597131,0.00008271469,0.0003732674,0.0001641367],"domain_scores_gemma":[0.9993527,0.00001405775,0.00006574827,0.0001122161,0.000357742,0.00009755829],"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":[0.000628964,0.0002248214,0.01305835,0.0009494633,0.0006140086,0.00008555794,0.08557906,0.09716272,0.5938922,0.005260114,0.01166885,0.1908759],"study_design_scores_gemma":[0.001380033,0.0002706812,0.9022872,0.0002247684,0.00003743127,0.00004365081,0.001646261,0.005109459,0.03731892,0.0002363314,0.05118112,0.0002640932],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9115452,0.00006735216,0.08195131,0.00005539988,0.0000502007,0.00007211071,0.00001120849,0.000017298,0.006229971],"genre_scores_gemma":[0.9774344,0.000043232,0.02235279,0.00001499869,0.00008539946,5.596839e-7,0.000009328853,0.00002370607,0.00003561069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8892289,"threshold_uncertainty_score":0.3159137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05091987550092156,"score_gpt":0.2797378371331725,"score_spread":0.2288179616322509,"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."}}