{"id":"W4236005182","doi":"10.35940/ijeat.b2281.129219","title":"Data Analyzing Immigration to Canada using Predictive Analysis Multiple Linear and Non Linear Regression","year":2019,"lang":"en","type":"article","venue":"International Journal of Engineering and Advanced Technology","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Workforce; Linear regression; Government (linguistics); Work (physics); Regression analysis; Population; Globalization; Entrepreneurship; Economics; Business; Economic growth; Demographic economics; Development economics; Geography; Engineering; Statistics; Demography; Sociology; Mathematics","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":[0.001361896,0.0005305922,0.0005435085,0.002952429,0.002757864,0.001481641,0.001354702,0.0004006947,0.009446346],"category_scores_gemma":[0.008839005,0.0002397678,0.0009268165,0.006536524,0.0005496326,0.0003071574,0.001111383,0.001465476,0.001222463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008481549,"about_ca_system_score_gemma":0.02320612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9595441,"about_ca_topic_score_gemma":0.9520447,"domain_scores_codex":[0.9983996,0.0001881417,0.000120387,0.000221598,0.0007284078,0.0003419485],"domain_scores_gemma":[0.9945366,0.001222178,0.0005943527,0.0003487965,0.002951936,0.0003460768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001529182,0.0002508058,0.9086375,0.000185133,0.0001740053,0.000572016,0.001516457,0.006544954,0.0003400618,0.002467866,0.03329974,0.0458584],"study_design_scores_gemma":[0.00001751496,0.0001057969,0.9241899,0.0002541664,0.0001035578,0.0001796142,0.009261254,0.02444136,0.0008000685,0.0007444928,0.03982579,0.00007649988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.848051,0.000484668,0.007874054,0.001390266,0.0001416487,0.0007492073,0.1206293,0.0004472263,0.02023267],"genre_scores_gemma":[0.894441,0.0008115961,0.01147883,0.0002280626,0.0000517708,0.0008990757,0.06657036,0.0001124854,0.02540679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04045588,"threshold_uncertainty_score":0.08138824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141259489890415,"score_gpt":0.2918315651229825,"score_spread":0.2804189702240784,"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."}}