{"id":"W2085551846","doi":"10.1061/(asce)1076-0342(2008)14:3(272)","title":"Discussion of “Prediction of Water Pipe Asset Life Using Neural Networks” by D. Achim, F. Ghotb, and K. J. McManus","year":2008,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Water Systems and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Asset (computer security); Engineering; Operations research; Computer science; Artificial intelligence; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002073825,0.0001594713,0.0004588662,0.0001414075,0.00005372919,0.00002202417,0.00009678776,0.0001498885,0.000007279762],"category_scores_gemma":[0.00001227265,0.0000862485,0.00007665803,0.00009846496,0.00003732125,0.0003314852,0.00002114536,0.000203268,9.262872e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004353574,"about_ca_system_score_gemma":0.00001298068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002977693,"about_ca_topic_score_gemma":0.000001290762,"domain_scores_codex":[0.9985139,0.00006871128,0.0009029452,0.00008171145,0.0002718807,0.0001608017],"domain_scores_gemma":[0.9992418,0.00001253527,0.0003754494,0.0001179424,0.0001478383,0.0001043831],"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":[0.00002557183,0.000006585443,0.01170816,0.0002264185,0.00007791345,0.000006140056,0.0006173512,0.9522592,0.02755193,0.000001693664,0.007442412,0.00007658221],"study_design_scores_gemma":[0.0008062347,0.0001583131,0.008903227,0.0003407666,0.00006126887,0.001105335,0.0002473295,0.9815636,0.005895515,0.000005687849,0.0007684886,0.0001442322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9254702,0.001136436,0.07089015,0.00001771307,0.002252889,0.000129429,0.00003592498,0.00001813544,0.00004907168],"genre_scores_gemma":[0.9990317,0.00007585077,0.0003375234,0.000004152695,0.0004830033,6.216343e-7,0.00001302459,0.0000253543,0.00002880597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07356141,"threshold_uncertainty_score":0.3517112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00692616076646919,"score_gpt":0.1820893027280745,"score_spread":0.1751631419616053,"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."}}