{"id":"W1656236262","doi":"10.24908/pceea.v0i0.3133","title":"The Leader-Engineer- Capabilities, Competencies, and Attributes","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Competence (human resources); Engineering ethics; Engineering education; Leverage (statistics); Leadership development; Engineering; Engineering management; Knowledge management; Computer science; Management; Political science; Public relations; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004432529,0.0001915663,0.0001484948,0.0001566411,0.0002835766,0.0001810178,0.0003346625,0.0001548694,0.00001973559],"category_scores_gemma":[0.0008571335,0.0001592051,0.00005685998,0.0004307129,0.0000408518,0.0001603233,0.00002220082,0.0004712947,0.00000735801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000887198,"about_ca_system_score_gemma":0.0004122092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001170211,"about_ca_topic_score_gemma":0.006727963,"domain_scores_codex":[0.9988976,0.000003179332,0.0002970132,0.0001476432,0.0002699175,0.0003846996],"domain_scores_gemma":[0.9990364,0.0001177945,0.00009582085,0.0001403623,0.0003732407,0.0002363472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003293278,0.0001523985,0.204403,0.001869672,0.0006712975,2.167506e-7,0.01008705,0.01163479,0.05519588,0.2510335,0.4541702,0.01077869],"study_design_scores_gemma":[0.0001928737,0.00001021918,0.2978179,0.0001114791,0.00004553115,0.0000162129,0.001122221,0.01200139,0.005132602,0.0003233551,0.6826677,0.0005585341],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712172,0.001021506,0.00003871497,0.01049869,0.0102367,0.0006382329,0.00004186925,0.0004757445,0.005831298],"genre_scores_gemma":[0.9959957,0.00006015916,0.001353747,0.00008631842,0.000220664,0.000101007,0.000008096601,0.00004386447,0.002130445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2507101,"threshold_uncertainty_score":0.6492193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003785989534667353,"score_gpt":0.1673425486874692,"score_spread":0.1635565591528019,"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."}}