{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001247871,0.0003045361,0.0004177167,0.00008279718,0.00004314422,0.0000808557,0.0001652023,0.0007836611,0.001689282],"category_scores_gemma":[0.00003071726,0.0002717689,0.0001181454,0.00005499818,0.00005490124,0.00005636866,0.00001186919,0.001112769,0.0001073854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006313901,"about_ca_system_score_gemma":0.0001422289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004160341,"about_ca_topic_score_gemma":0.003122641,"domain_scores_codex":[0.9988335,0.000007096794,0.0003453333,0.0001815181,0.000355811,0.0002767717],"domain_scores_gemma":[0.9993612,0.00004698617,0.00003514156,0.0003507029,0.00009382416,0.0001121505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001590327,0.00004832828,0.009391428,0.0003700992,0.0002502821,0.0002036036,0.00003687023,0.0001245087,0.0003341536,0.000299541,0.9832405,0.00568472],"study_design_scores_gemma":[0.0002092778,0.00001178912,0.01677247,0.00002163266,0.00009047153,0.00002346769,7.775856e-7,0.0001570265,0.00006737847,0.00001856377,0.9822532,0.0003739368],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01190833,0.0004267554,0.01483789,0.00002088383,0.03590224,0.0004464666,0.001639029,0.001734946,0.9330835],"genre_scores_gemma":[0.9151326,0.003765768,0.004309106,0.0001035017,0.01856533,0.00004043325,0.005037266,0.000498815,0.05254712],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9032243,"threshold_uncertainty_score":0.9999735,"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."}}