{"id":"W7068670907","doi":"","title":"Ability drain","year":2015,"lang":"en","type":"report","venue":"Cadmus - EUI Research Repository (European University Institute)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Brain drain; Vetting; Human capital; Immigration; Capital (architecture); Inequality","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","metaepi_narrow","sts"],"category_scores_codex":[0.03451397,0.0013733,0.001642738,0.003058531,0.003076294,0.0007231339,0.005421601,0.001088701,0.0001412736],"category_scores_gemma":[0.01307002,0.001581149,0.000917684,0.003170166,0.006454831,0.001685941,0.004678006,0.007583415,0.01375266],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.04399564,"about_ca_system_score_gemma":0.03348382,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01173326,"about_ca_topic_score_gemma":0.001868248,"domain_scores_codex":[0.967158,0.01190009,0.001319971,0.003440786,0.01347261,0.002708582],"domain_scores_gemma":[0.9706304,0.0005875754,0.0009588217,0.005441078,0.01960205,0.002780079],"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":[0.0006162156,0.0006965478,0.001213242,0.0006950906,0.0006705126,0.05941094,0.0009240788,0.0001000842,0.002039839,0.002167096,0.927968,0.003498319],"study_design_scores_gemma":[0.001394124,0.000319873,0.003475744,0.0006120093,0.0002605597,0.00130696,0.001235744,0.00004459316,0.0002675402,0.00005058272,0.9895693,0.00146295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.045074,0.001037585,0.00004691012,0.0001225999,0.005241483,0.001939263,0.0008055356,0.001367313,0.9443653],"genre_scores_gemma":[0.4767652,0.0007498794,0.0002076932,0.00002320331,0.005379583,0.000007106083,0.001414668,0.0007849017,0.5146677],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4316913,"threshold_uncertainty_score":0.9999595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2427368865691095,"score_gpt":0.3776391558457667,"score_spread":0.1349022692766573,"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."}}