{"id":"W2395355970","doi":"10.1061/9780784479827.178","title":"A Fuzzy Aggregation Method for Measuring Construction Crew Motivation","year":2016,"lang":"en","type":"article","venue":"Construction Research Congress 2016","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Crew; Computer science; Productivity; Fuzzy logic; Fuzzy set; Operations research; Artificial intelligence; Engineering; Aeronautics","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":[0.002385256,0.0001969764,0.0002460228,0.000589374,0.0005082427,0.0001005393,0.0002631177,0.0002568466,0.0008668283],"category_scores_gemma":[0.0008990646,0.0001484205,0.0001105324,0.0004222765,0.0006404875,0.0004874477,0.00006633338,0.0002750969,0.0002771273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002498909,"about_ca_system_score_gemma":0.0002004365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001180807,"about_ca_topic_score_gemma":0.00002782673,"domain_scores_codex":[0.9971759,0.0005297391,0.0004640005,0.0006202798,0.000545052,0.0006650049],"domain_scores_gemma":[0.9964912,0.001263224,0.0002373051,0.0005223007,0.00131598,0.0001699691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003380065,0.00003169156,0.01328253,0.00003849417,0.00008347462,0.000001887039,0.0001588616,0.000003137024,0.006481691,0.1562246,0.003528739,0.8198269],"study_design_scores_gemma":[0.02868328,0.002026257,0.1130388,0.003240321,0.0002067027,0.001585464,0.006687923,0.005624318,0.0450339,0.3586398,0.432121,0.003112266],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3918073,0.0005573023,0.5470644,0.004477662,0.01174078,0.002825371,0.0003126532,0.0004574747,0.04075703],"genre_scores_gemma":[0.9388381,0.0002829119,0.04650766,0.00005885446,0.001268485,0.0009638396,0.00003732294,0.00008497541,0.01195787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8167146,"threshold_uncertainty_score":0.9491162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1102315650383798,"score_gpt":0.4127732549972956,"score_spread":0.3025416899589158,"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."}}