Bibliographic record
Abstract
The Arabs in general are genetically diverse. Major factors that contributed to their diversity include the migrations of Semitic tribes from the Arabian Peninsula, the Islamic expansion in the 7th century AD, the Crusade wars and the recent migration dynamics. These events have resulted in the admixture of the original Arabs with other populations extending from east and south Asia to Europe and Africa. Their demographic features include high rates of consanguinity, a large family size and a rapid population growth. There is a high frequency of autosomal recessive disorders and increased frequencies of homozygosity for autosomal dominant traits, such as familial hypercholesterolemia and X-linked traits, such as glucose-6-phosphate dehydrogenase deficiency. The patterns of autosomal recessive disorders, including their mutations, may be different in various geographic locations within the Arab world. However, there are disorders that are specifically prevalent among the Arabs either uniformly or in certain locations. The Arab Genetic diseases include Bardet-Biedl syndrome, Meckel syndrome, autosomal recessive severe childhood muscular dystrophy, osteopetrosis and renal tubular acidosis, Sanjad-Sakati syndrome and others.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".