{"id":"W3119922532","doi":"10.1080/10298436.2020.1866759","title":"Life cycle analysis for asphalt pavement in Canadian context: modelling and application","year":2021,"lang":"en","type":"article","venue":"International Journal of Pavement Engineering","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Context (archaeology); Engineering; Life-cycle assessment; Pavement engineering; Transport engineering; Regression analysis; Civil engineering; Asphalt pavement; Asphalt; Production (economics); Computer science; Machine learning; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004972277,0.0001228602,0.0002006874,0.0006237128,0.00002249128,0.00005552777,0.0001406537,0.0000455289,0.00003590451],"category_scores_gemma":[0.00005101593,0.0001417571,0.00008678048,0.0002456282,0.000005541298,0.0002715562,0.00001666312,0.000114264,0.000001588493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005348295,"about_ca_system_score_gemma":0.0001399302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008289366,"about_ca_topic_score_gemma":0.006099646,"domain_scores_codex":[0.9987524,0.000008575052,0.0005624632,0.0001180586,0.0003366573,0.00022183],"domain_scores_gemma":[0.9992583,0.00005258235,0.00009816333,0.00008468354,0.0002973978,0.0002088471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003113337,0.00001637279,0.007844949,0.00002902679,0.0005606703,0.00000832975,0.0002091275,0.9868326,0.001944036,0.000498953,0.00002213836,0.002030689],"study_design_scores_gemma":[0.0006916253,0.00001836594,0.003380745,0.00005583445,0.00009096755,0.000004086828,0.0001348625,0.9876691,0.004654496,0.00007355097,0.003100417,0.0001259562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3460561,0.0007785487,0.6522361,0.0002608477,0.0004639397,0.0001403901,0.00001529753,0.00001584318,0.00003295861],"genre_scores_gemma":[0.9944527,0.0002542097,0.00485965,0.0001313683,0.0001957085,0.00003313934,0.0000452632,0.00001898593,0.000009011967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6483966,"threshold_uncertainty_score":0.5780686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358458496259471,"score_gpt":0.2523841657054459,"score_spread":0.2387995807428512,"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."}}