{"id":"W6977129908","doi":"10.6084/m9.figshare.7775537","title":"Additional file 3: of Barriers and recruitment strategies for precarious status migrants in Montreal, Canada","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Immigration; Government (linguistics); Data collection","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001467595,0.0008812089,0.0009072345,0.003042712,0.002707026,0.001674764,0.001965553,0.0007366875,0.6948132],"category_scores_gemma":[0.0247804,0.0004544664,0.0009617766,0.005138455,0.0003745933,0.001552233,0.000988521,0.0008003928,0.03985619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009331212,"about_ca_system_score_gemma":0.02252537,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8146984,"about_ca_topic_score_gemma":0.8944226,"domain_scores_codex":[0.999355,0.00008519434,0.00008682362,0.00009417787,0.0001899262,0.00018888],"domain_scores_gemma":[0.9862308,0.007370883,0.0006147196,0.0004312788,0.00480528,0.0005470613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001176734,0.00005521421,0.009929446,0.001538087,0.00003024171,0.0000550634,0.0005444278,0.0003434138,0.00002965811,0.0004530733,0.9739502,0.01295357],"study_design_scores_gemma":[0.00316067,0.000197793,0.2892751,0.01393676,0.0003687749,0.0003543138,0.02036115,0.004275168,0.0007335423,0.00488446,0.6620532,0.000399041],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001745731,0.00004776565,0.0001919508,0.0003058767,0.00003768013,0.0002849424,0.9936827,0.0001179773,0.003585342],"genre_scores_gemma":[0.08455697,0.0008490192,0.008065778,0.00107391,0.0001019416,0.01021337,0.8498476,0.0004727042,0.04481864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6948132,"threshold_uncertainty_score":0.4353119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436020640592271,"score_gpt":0.2002270763277389,"score_spread":0.1858668699218162,"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."}}