Grammatical Equivalence of Animal Science Terms Translation
Bibliographic record
Abstract
Translating specific language for a special subject such as animal science terms should have an understanding of the knowledge. The results of translation in their forms also give effect to their meaning in order to obtain the equivalence and adaptation from the source language (SL) into the target language (TL). This study aims at finding equivalence in the form of translation including their effect of meaning translated from English (SL) into Indonesian (TL). Qualitative method is used to analyze the translation of languages with a descriptive explanation. Both languages have their own grammatical rules which have varieties of translation, especially for the result of findings in TL. The grammatical equivalence found in numbers of nouns and noun phrases. Majorly, they were found with the suffix –s for the plural form in SL and translated without reduplication in TL to show their adaptation as a scientific language. In some cases, the terms in SL were translated into collective words and conjunction. It showed in scientific languages, both languages have their own rules to give equivalence of forms in SL and TL including their meaning.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".