{"id":"W4253186889","doi":"10.4095/301181","title":"Commercial Land Use: Arterial Strips","year":2010,"lang":"en","type":"report","venue":"","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"STRIPS; Environmental science; Computer science; Artificial intelligence","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":[0.00006904482,0.0004379838,0.0001396011,0.00301351,0.0005575665,0.001059417,0.0003895922,0.0001902837,0.03366065],"category_scores_gemma":[0.0003786056,0.0001396414,0.0002370759,0.006789832,0.0002790321,0.0003332915,0.0005193739,0.0002256389,0.007543249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000964046,"about_ca_system_score_gemma":0.001181491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3765445,"about_ca_topic_score_gemma":0.545717,"domain_scores_codex":[0.9997843,0.00001778446,0.000008489083,0.00002659066,0.0001016269,0.00006120271],"domain_scores_gemma":[0.9996448,0.00002471827,0.00004228815,0.00001563158,0.000227131,0.00004541047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003914348,0.0002483299,0.3940641,0.001072236,0.0001131026,0.00103237,0.007369852,0.004459364,0.004476978,0.004744791,0.3673991,0.2146283],"study_design_scores_gemma":[0.00001173168,0.00002946958,0.7524869,0.0001206966,0.000009408422,0.0002283083,0.005555116,0.0009334178,0.0003158383,0.0002454275,0.2400364,0.0000270786],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2842848,0.0009585245,0.001542843,0.0003980488,0.0001265646,0.0005806747,0.3056856,0.0007075371,0.4057153],"genre_scores_gemma":[0.7458594,0.001432439,0.005326798,0.00007213579,0.00003920078,0.0005735155,0.1191643,0.0001753603,0.1273569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3765445,"threshold_uncertainty_score":0.7487056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07661385768835259,"score_gpt":0.2366543875687442,"score_spread":0.1600405298803916,"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."}}