{"id":"W4401324212","doi":"10.31224/3837","title":"Designing a Collaborative Algorithm for Performance Evaluation of Construction Companies, using Content analysis","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Reliability (semiconductor); Rank (graph theory); Content analysis; Process management; Data mining; Engineering; Machine learning; Mathematics","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"],"consensus_categories":[],"category_scores_codex":[0.008610674,0.0003119852,0.0008667937,0.002309838,0.0002137454,0.0004895918,0.0004896406,0.0001898469,0.0005536755],"category_scores_gemma":[0.0003973953,0.0002506951,0.0004372265,0.003692783,0.0002378469,0.0002913739,0.0006360406,0.000289842,0.00001535202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668131,"about_ca_system_score_gemma":0.0009195735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001189527,"about_ca_topic_score_gemma":0.00009201781,"domain_scores_codex":[0.9946841,0.0003377758,0.001496336,0.0008800323,0.002365571,0.0002362001],"domain_scores_gemma":[0.9921999,0.0004507031,0.001197142,0.0006306643,0.005465914,0.00005569646],"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":[0.00005849445,0.00002748954,0.004637381,0.0001149375,0.002362312,2.628518e-7,0.0008943861,0.1135175,0.0002488604,0.001225348,0.0003872216,0.8765258],"study_design_scores_gemma":[0.0004620162,0.00005393372,0.0007832639,0.0001078307,0.004081025,0.000002654374,0.004064924,0.9792947,0.002773978,0.007778486,0.0003253751,0.0002718571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4454455,0.0006627192,0.5462568,0.00006824738,0.002287699,0.00200398,0.0002351213,0.00006551216,0.002974425],"genre_scores_gemma":[0.7518838,0.00006336075,0.2471135,0.00001657161,0.0001335548,0.0001727428,0.00008010448,0.00001571301,0.0005206771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8762539,"threshold_uncertainty_score":0.9999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3629066144669747,"score_gpt":0.4416612588400544,"score_spread":0.07875464437307972,"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."}}