{"id":"W4407002447","doi":"10.1007/978-3-031-79038-6_13","title":"Gender-Neutral English to Portuguese Machine Translator: Promoting Inclusive Language","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Portuguese; Computer science; Natural language processing; Linguistics; Artificial intelligence; Programming language; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003683544,0.0006205696,0.0003995849,0.0005986009,0.001742976,0.002658322,0.0009331968,0.0008815448,0.02145791],"category_scores_gemma":[0.011787,0.0003193044,0.0002444909,0.0005063125,0.001265391,0.003803001,0.003692769,0.001674359,0.01197354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006133047,"about_ca_system_score_gemma":0.003088378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009574732,"about_ca_topic_score_gemma":0.002216227,"domain_scores_codex":[0.9969831,0.001537692,0.0001274424,0.0004066152,0.0007058118,0.0002392774],"domain_scores_gemma":[0.9943103,0.002535236,0.0003056962,0.0009574063,0.001395122,0.0004962744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006419639,0.0004080907,0.0030284,0.001190461,0.00001756076,0.002721792,0.0584455,0.0007728814,0.06181962,0.09871517,0.1178745,0.654364],"study_design_scores_gemma":[0.0000979764,0.0005043408,0.006162884,0.001025595,0.00008198947,0.004293121,0.02585565,0.00631817,0.06020645,0.04400806,0.8513273,0.0001186106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.1768835,0.001835993,0.2192495,0.01498242,0.004031477,0.0004532116,0.001430721,0.006221928,0.5749113],"genre_scores_gemma":[0.7344297,0.001368819,0.08852552,0.003017077,0.0007001492,0.0003456714,0.001468313,0.006058376,0.1640864],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02145791,"threshold_uncertainty_score":0.07178384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257219906846625,"score_gpt":0.2616418444060497,"score_spread":0.2490696453375834,"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."}}