{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003261876,0.0002123975,0.00021314,0.000553131,0.0001562574,0.0005709762,0.000263836,0.00007262894,0.00003783413],"category_scores_gemma":[0.0006054932,0.0002248317,0.00005704792,0.000740707,0.0001564356,0.0007557489,0.0004116528,0.00008285597,0.00008600797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003060332,"about_ca_system_score_gemma":0.0001790854,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01642659,"about_ca_topic_score_gemma":0.0008287174,"domain_scores_codex":[0.9983801,0.00001868332,0.0003646581,0.0003306702,0.0003024659,0.0006034355],"domain_scores_gemma":[0.9992462,0.0000265597,0.0001486319,0.0002833203,0.0001892775,0.0001059868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004847843,0.00009950174,0.07408945,0.00005924474,0.00001867049,0.00001025462,0.00006516404,0.00009742766,5.051329e-7,0.9099372,0.01274975,0.002867988],"study_design_scores_gemma":[0.001949329,0.00001994119,0.04733132,0.00004941911,0.00004448648,0.00001224602,0.001287191,0.01952545,0.000002061992,0.05594268,0.8733912,0.0004446456],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8191145,0.0002288319,0.0005869057,0.01176216,0.0002610415,0.0003884983,0.00005528264,0.0001689237,0.1674338],"genre_scores_gemma":[0.9916126,0.00002658419,0.00007853813,0.006433244,0.001143871,0.00002624327,0.00007433752,0.00001601461,0.0005885884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8606415,"threshold_uncertainty_score":0.9901231,"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."}}