{"id":"W4410126451","doi":"10.47857/irjms.2025.v06i02.03259","title":"Mapping Dentists’ Language Skills Using Canadian Language Benchmarks in India","year":2025,"lang":"en","type":"article","venue":"International Research Journal of Multidisciplinary Scope","topic":"Interpreting and Communication in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Linguistics; Natural language processing; Medical education; Medicine; Philosophy","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":[0.002275341,0.0003640946,0.000271661,0.005478241,0.002560204,0.001697884,0.00114001,0.0003309983,0.001340725],"category_scores_gemma":[0.01229578,0.0003097151,0.0003507255,0.007324033,0.0009922212,0.000442877,0.001670101,0.0007073352,0.0003024153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01980761,"about_ca_system_score_gemma":0.03502405,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9273273,"about_ca_topic_score_gemma":0.9498116,"domain_scores_codex":[0.9961476,0.000448489,0.0002712545,0.0003652879,0.001866525,0.0009009133],"domain_scores_gemma":[0.9919194,0.001244841,0.001012408,0.0003581221,0.004520109,0.0009451659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003102623,0.0001862366,0.7746635,0.0006100328,0.00005936741,0.0008345342,0.07308082,0.0007905176,0.00216888,0.002415987,0.007964647,0.1369153],"study_design_scores_gemma":[0.000007204288,0.00006673666,0.9629301,0.00009848574,0.00001663036,0.0002360077,0.02648126,0.0004061222,0.0004897482,0.00008202568,0.009139724,0.00004593143],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835291,0.0003290862,0.0003649825,0.0004089555,0.00002052345,0.0001383806,0.001767263,0.00004737822,0.01339442],"genre_scores_gemma":[0.99635,0.0003055907,0.001050093,0.00008749111,0.000004064395,0.00007944275,0.001081037,0.0000144118,0.001027741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9273273,"threshold_uncertainty_score":0.1462014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.076877866525144,"score_gpt":0.5405690381694351,"score_spread":0.4636911716442911,"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."}}