{"id":"W4285263362","doi":"10.7764/ric.00023.21","title":"AN INTEGRATED INFRASTRUCTURE PRIORITIZATION MODEL: CASE STUDY OF TRIPOLI, LEBANON","year":2022,"lang":"en","type":"article","venue":"Revista Ingeniería de Construcción","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Weighting; Computer science; Operations research; Entropy (arrow of time); Sample (material); Prioritization; Analytic hierarchy process; Electricity; Process (computing); Engineering; Management science","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004414762,0.0003549661,0.0008171411,0.0008370201,0.0007081812,0.0006016829,0.001512288,0.0001227702,0.001363386],"category_scores_gemma":[0.002586841,0.0003192369,0.0001726047,0.002603517,0.0001610436,0.0005217965,0.0005889214,0.0006001329,0.000006774524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003725056,"about_ca_system_score_gemma":0.0006749014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002471464,"about_ca_topic_score_gemma":0.00008044828,"domain_scores_codex":[0.9926039,0.001820287,0.00194929,0.0009948876,0.002177869,0.0004537935],"domain_scores_gemma":[0.995418,0.0007463036,0.001010063,0.001675057,0.0008773854,0.0002731839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005718419,0.001118296,0.1748354,0.00005188797,0.0001413318,0.003269564,0.01974575,0.2024393,0.01419564,0.005538553,0.003074679,0.5750178],"study_design_scores_gemma":[0.003375754,0.001021095,0.002902106,0.00005133586,0.000152255,0.006937855,0.07159784,0.8975326,0.0003618377,0.00518377,0.01006873,0.0008147674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9362394,0.0002070061,0.0618174,0.00002234626,0.0003369527,0.0008866892,0.0002056621,0.00009934799,0.0001851848],"genre_scores_gemma":[0.9853023,0.000005128108,0.01430455,0.0001178311,0.00006241434,0.00005633783,0.000018417,0.00004784125,0.00008523918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6950934,"threshold_uncertainty_score":0.999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08017934806153548,"score_gpt":0.4080304470722354,"score_spread":0.3278510990106999,"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."}}