{"id":"W2318965785","doi":"10.1557/opl.2012.117","title":"Statistical Analysis Based on Information Obtained from Inspection in Line to Forecast Future Land Pipeline Damage","year":2012,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pipeline (software); Pipeline transport; Fractal; Artificial neural network; Series (stratigraphy); Fractal analysis; Time series; Software; Data mining; Computer science; Operations research; Fractal dimension; Artificial intelligence; Engineering; Machine learning; Geology; Mathematics; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001985839,0.0006384799,0.0005939331,0.002673286,0.0002090477,0.0005289402,0.000460183,0.0004532263,0.001386906],"category_scores_gemma":[0.007488852,0.0002542526,0.0008048954,0.001424084,0.0002811656,0.0008028402,0.000253132,0.0004542973,0.0004257507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000550536,"about_ca_system_score_gemma":0.0006299228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007411519,"about_ca_topic_score_gemma":0.008789086,"domain_scores_codex":[0.9993105,0.0001627523,0.00006171445,0.0001439979,0.0002545378,0.00006646825],"domain_scores_gemma":[0.9908894,0.005832572,0.001009621,0.0005608638,0.001536741,0.0001707723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001078792,0.00044679,0.2231245,0.0001108959,0.0003396031,0.0002153508,0.00005373,0.6581129,0.01055921,0.001047403,0.001737002,0.1031737],"study_design_scores_gemma":[0.000009242022,0.0002685848,0.0637229,0.000006129338,0.00005792069,0.00004178329,0.00001818805,0.9330555,0.002117134,0.0004950067,0.0001881788,0.0000194159],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8405148,0.0002367935,0.1538455,0.0001852398,0.00004536823,0.00006519519,0.002038471,0.0008534908,0.002215082],"genre_scores_gemma":[0.9883364,0.00007917442,0.009292173,0.00001463645,0.00002103507,0.0000237863,0.001526097,0.00001921012,0.0006874907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007411519,"threshold_uncertainty_score":0.01473671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005566058890630859,"score_gpt":0.1961654125529387,"score_spread":0.1905993536623078,"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."}}