{"id":"W4412889209","doi":"10.18653/v1/2025.bea-1.33","title":"LangEye: Toward ‘Anytime’ Learner-Driven Vocabulary Learning From Real-World Objects","year":2025,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Vocabulary; Vocabulary learning; Human–computer interaction; Artificial intelligence; Multimedia; Natural language processing; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019483,0.0008865319,0.0004869667,0.0006016378,0.0002617085,0.001461276,0.001990035,0.001013334,0.006504199],"category_scores_gemma":[0.006428849,0.0004237169,0.0006857021,0.0002634281,0.0005242961,0.003154,0.003679472,0.0009347185,0.003845347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002834771,"about_ca_system_score_gemma":0.0005767541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006843393,"about_ca_topic_score_gemma":0.002382926,"domain_scores_codex":[0.9990782,0.0003434441,0.00005035431,0.0002298334,0.0002329677,0.00006518034],"domain_scores_gemma":[0.9977201,0.001411962,0.00009913764,0.0003756604,0.0002416953,0.0001514506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007768202,0.001043033,0.005531498,0.00264707,0.0001510627,0.001514516,0.009150399,0.008817183,0.1236032,0.01154853,0.03746075,0.797756],"study_design_scores_gemma":[0.0004369683,0.002332781,0.01081637,0.0008805345,0.0002460867,0.004669621,0.006581845,0.1560607,0.2084277,0.03767947,0.5714242,0.0004436067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0690628,0.001112629,0.8782616,0.0004393551,0.0001076687,0.0005850184,0.0008865597,0.03974488,0.009799373],"genre_scores_gemma":[0.2437751,0.0009057734,0.7280039,0.0004343485,0.0000558371,0.001070802,0.003011448,0.002637781,0.020105],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006504199,"threshold_uncertainty_score":0.02175874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01600738205086413,"score_gpt":0.2846271985594598,"score_spread":0.2686198165085957,"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."}}