{"id":"W2912647078","doi":"","title":"Inspire, Prepare, Connect: Building a Talent Pipeline","year":2015,"lang":"en","type":"article","venue":"Bridges Conversations in Global Politics and Public Policy","topic":"Human Resource and Talent Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Center (category theory); Engineering; Engineering management; Management; Political science; Sociology; Mathematics education; Psychology; Mechanical engineering; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003522803,0.0005002474,0.0001629114,0.000799373,0.01145975,0.007671997,0.001275619,0.005052248,0.06115865],"category_scores_gemma":[0.006361551,0.000582732,0.000283163,0.0006247016,0.002730991,0.01259917,0.009281004,0.005467817,0.02359048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00302713,"about_ca_system_score_gemma":0.01765095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122206,"about_ca_topic_score_gemma":0.06252214,"domain_scores_codex":[0.998768,0.000315212,0.00001911421,0.00008818804,0.0002692004,0.0005401348],"domain_scores_gemma":[0.9916506,0.0004827472,0.0001146024,0.0001831919,0.0007091979,0.00685964],"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":[0.00001224073,0.00008534993,0.0008081702,0.0000212344,0.000001424359,0.00006356893,0.001696594,0.00005744677,0.0001214845,0.007292086,0.9482319,0.04160849],"study_design_scores_gemma":[0.00001464702,0.00006564063,0.001487698,0.00008594382,0.000003432661,0.00004892165,0.007360648,0.0001977361,0.0002024293,0.007461792,0.9830494,0.00002171164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02233716,0.001991729,0.005188728,0.6828952,0.00544042,0.0001709922,0.0003891307,0.002717165,0.2788695],"genre_scores_gemma":[0.1768806,0.003869861,0.02725238,0.1365337,0.00200876,0.0005685466,0.001407358,0.001367404,0.6501115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06115865,"threshold_uncertainty_score":0.204596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03363469119539989,"score_gpt":0.2765719841182013,"score_spread":0.2429372929228014,"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."}}