{"id":"W7130690259","doi":"10.5281/zenodo.18713444","title":"Migration, Human Capital Formation and Economic Growth in Nigeria","year":2015,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human capital; Incentive; Cointegration; Distributed lag; Ordinary least squares; Population; Physical capital; Stock (firearms); Immigration","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004112876,0.000205001,0.0001725671,0.0008052901,0.000404089,0.0008667797,0.00011082,0.0001792346,0.001113397],"category_scores_gemma":[0.001072896,0.00009066837,0.0001525589,0.00123486,0.0003390281,0.0006032705,0.0006147868,0.0004030767,0.00009923408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008044377,"about_ca_system_score_gemma":0.0008266005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01694645,"about_ca_topic_score_gemma":0.02306725,"domain_scores_codex":[0.9998574,0.00003809556,0.00001536103,0.00001735603,0.00001557911,0.0000561242],"domain_scores_gemma":[0.9994965,0.0001676509,0.0001866561,0.00001431176,0.00005998027,0.00007497955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001816928,0.0001701194,0.9533146,0.0001305641,0.00003246389,0.001156758,0.00228641,0.004727497,0.0004597566,0.007129021,0.0006414876,0.02976959],"study_design_scores_gemma":[0.000008553902,0.0001141935,0.9797662,0.0002090532,0.00002765851,0.0003401687,0.007824204,0.004398154,0.0003437666,0.002416122,0.004539314,0.00001245944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948579,0.001079807,0.0001514574,0.0002327665,0.00001265639,0.000008118487,0.00017122,0.000002492236,0.003483574],"genre_scores_gemma":[0.998396,0.0007210988,0.0001144232,0.00001146558,0.000004482866,0.000006079031,0.0001025469,5.718358e-7,0.0006433488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01694645,"threshold_uncertainty_score":0.03369564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536005619650744,"score_gpt":0.2871623875391457,"score_spread":0.2618023313426383,"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."}}