{"id":"W2271579450","doi":"10.1061/(asce)cf.1943-5509.0000866","title":"Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains","year":2016,"lang":"en","type":"article","venue":"Journal of Performance of Constructed Facilities","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Mains electricity; Rehabilitation; Sanitary sewer; Engineering; Predictive modelling; Civil engineering; Pipeline transport; Driver rehabilitation; Forensic engineering; Computer science; Environmental engineering","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":[0.0002197037,0.0001035696,0.0002717013,0.0002534738,0.00001620098,0.000004608891,0.00008272793,0.0001022423,0.0000354474],"category_scores_gemma":[0.0001894708,0.00007717451,0.00009097755,0.0001047533,0.0001404181,0.0003368456,0.000005134171,0.000112869,2.819556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007321363,"about_ca_system_score_gemma":0.00004289543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001833914,"about_ca_topic_score_gemma":0.000002256761,"domain_scores_codex":[0.9989728,0.00001336955,0.0006380011,0.00005811037,0.0001823356,0.0001354004],"domain_scores_gemma":[0.9992146,0.0002459439,0.0001580281,0.00009427706,0.0002536,0.00003352082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001145345,0.00009096626,0.02919901,0.005379324,0.0003296781,0.0000019626,0.002311772,0.4632605,0.4478118,0.0120568,0.001454086,0.03695869],"study_design_scores_gemma":[0.02099239,0.00954949,0.3375009,0.00774637,0.0003100356,0.0005561503,0.008993614,0.1663736,0.3953654,0.02331252,0.02766919,0.001630343],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750162,0.0001239039,0.02391971,0.00004551541,0.0004236108,0.0001055799,0.0001753141,0.00003071216,0.0001594257],"genre_scores_gemma":[0.9971687,0.0001402157,0.002591556,0.000001045766,0.0000365091,0.000004763197,0.000003254165,0.000008387998,0.00004559783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3083019,"threshold_uncertainty_score":0.3147085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00407422152534737,"score_gpt":0.1911306261507556,"score_spread":0.1870564046254082,"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."}}