Experimental Animal Use in Turkey: A Comparison with Other Countries
Bibliographic record
Abstract
The legal structure that governs animal use in Turkey is in line with that of the European Union (EU). In 2004, legislation on the use of animals for experimental and other scientific purposes was established in Turkey for the first time. The present study aimed to compare the data on experimental animal use in Turkey (during the period 2008-2017) with similar reports from selected countries (the United States, Australia, Canada and the EU). In Turkey, a total of 2,104,828 animals were used for experimental and other scientific purposes during 2008-2017. Of the animals used, 758,887 were fish (36%), 433,417 rats (21%), 302,512 birds other than quail (14%) and 285,531 mice (13%). According to a breakdown by purpose for use, in Turkey during 2009-2017, out of a total number of 1,955,307 animals used, 56% were for fundamental biological studies, with a high proportion used for research on animal disease. Compared with the other countries, fewer animals were used in Turkey although the national trend seems to indicate that the number is fluctuating. Further studies are required to uncover the reasons behind this reduced animal use in Turkey, as compared to other countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".