Translating 3D Slicer into Brazilian Portuguese: A methodological approach to software localization in Latin America
Bibliographic record
Abstract
Abstract 3D Slicer is an open-source software platform for the analysis, segmentation, and three-dimensional visualization of medical imaging data. Although the platform is used by an international research community, its interface was historically available primarily in English, which may limit accessibility for non-English-speaking users. This study describes the development of an ad hoc methodology for the Brazilian Portuguese localization of 3D Slicer within the broader Latin American localization initiative. The methodology addresses recurrent linguistic challenges identified in a preliminary corpus of 300 interface strings, including domain-specific vocabulary, acronyms, word order, passive voice, syntagms, and the adaptation of technical terms. The translation process emphasizes textual uniformity, cohesion, terminological accuracy, and contextual validation in biomedical-computational environments. The proposed framework may support similar software localization efforts in other non-English-speaking contexts, especially when technical precision and linguistic adaptation must be balanced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".