{"id":"W3121940051","doi":"","title":"Niveaux annuels d'immigration et gains initiaux des immigrants au Canada","year":2014,"lang":"fr","type":"article","venue":"Direction des études analytiques : documents de recherche","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Political science; Humanities; Concurrence; Art","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":[0.002231632,0.0002979533,0.000404918,0.001394623,0.002800578,0.002120024,0.0007449268,0.0005322511,0.005537504],"category_scores_gemma":[0.00815127,0.000171844,0.0005029603,0.00169982,0.001005394,0.0005430541,0.00133295,0.001269537,0.0004633706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0141508,"about_ca_system_score_gemma":0.0369062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9403898,"about_ca_topic_score_gemma":0.9682148,"domain_scores_codex":[0.9986426,0.0001759063,0.00003718744,0.000161182,0.0004055781,0.0005775713],"domain_scores_gemma":[0.9948142,0.0009741727,0.0005214133,0.0001746562,0.002049533,0.001466121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008641926,0.0001434535,0.9148231,0.00009662529,0.00009279859,0.0002051367,0.01070471,0.0009849954,0.0006428351,0.003521246,0.002529403,0.06539153],"study_design_scores_gemma":[0.00001896189,0.00007549554,0.9843322,0.0001161078,0.00005511722,0.00004550175,0.008060411,0.0003782752,0.0002968037,0.0003992714,0.006200563,0.00002121115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889145,0.0009260769,0.0002435955,0.0007316857,0.00002099883,0.00002817298,0.0008032352,0.00001579881,0.008315928],"genre_scores_gemma":[0.9856089,0.001245617,0.0004198641,0.000145435,0.00001450167,0.00004225613,0.0006910925,0.00001671034,0.0118157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05961019,"threshold_uncertainty_score":0.1199225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245957224779373,"score_gpt":0.4146234467868178,"score_spread":0.2900277243088805,"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."}}