{"id":"W6983446901","doi":"","title":"Migration and Canadian Interprovincial Trade","year":2022,"lang":"en","type":"other","venue":"RePEc: Research Papers in Economics","topic":"Garlic and Onion Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock (firearms); Immigration; Panel data; Estimation; Border effect; Trade agreement","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":[0.0004268177,0.0003109447,0.0002337954,0.002345945,0.001653558,0.001248755,0.0005073083,0.0001741892,0.004152528],"category_scores_gemma":[0.002025657,0.0001267185,0.0005844596,0.005737004,0.0003967556,0.0004226757,0.0008400917,0.0004168053,0.000298641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01501487,"about_ca_system_score_gemma":0.01830034,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940896,"about_ca_topic_score_gemma":0.9958347,"domain_scores_codex":[0.9996112,0.00002977009,0.0000169927,0.00005891078,0.0001193038,0.0001637811],"domain_scores_gemma":[0.9986872,0.0001383668,0.0002803524,0.0000636306,0.000633303,0.0001970783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001027469,0.00001474808,0.9639262,0.00007831269,0.0001439918,0.0001469796,0.001143476,0.002776455,0.0001841413,0.002593089,0.005447234,0.02344263],"study_design_scores_gemma":[0.000004946794,0.000007797489,0.9864866,0.00005237291,0.00004989477,0.00004341196,0.001503139,0.001313607,0.0001496845,0.0001716901,0.01019964,0.00001733453],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635164,0.002177149,0.0005750075,0.0007345535,0.00003049184,0.00001661897,0.01583144,0.00004283552,0.01707548],"genre_scores_gemma":[0.9882486,0.001134701,0.0004023173,0.00005339555,0.000008121558,0.000006358291,0.005492597,0.000008761455,0.004645244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01501487,"threshold_uncertainty_score":0.108941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218777916438514,"score_gpt":0.2545852289965944,"score_spread":0.232707437352743,"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."}}