The Israeli–Palestinian wheat landraces collection: restoration and characterization of lost genetic diversity
Bibliographic record
Abstract
BACKGROUND: For over a century, genetic diversity of wheat worldwide was eroded by continual selection for high yields and industrial demands. Wheat landraces cultivated in Israel and Palestine demonstrate high genetic diversity and a potentially wide repertoire of adaptive alleles. While most Israeli-Palestinian wheat landraces were lost in the transition to 'Green Revolution' semi-dwarf varieties, some germplasm collections made at the beginning of the 20th century survived in gene banks and private collections worldwide. However, fragmentation and poor conservation place this unique genetic resource at a high risk of genetic erosion. Herein, we describe a long-term initiative to restore, conserve, and characterize a collection of Israeli and Palestinian wheat landraces (IPLR). RESULTS: We report on (i) the IPLR construction (n = 932), (ii) the historical and agronomic context to this collection, (iii) the characterization and assessment of the IPLR's genetic diversity, and (iv) a data comparison from two distinct subcollections within IPLR: a collection made by N. Vavilov in 1926 (IPLR-VIR) and a later one (1979-1981) made by Y. Mattatia (IPLR-M). Though conducted in the same eco-geographic space, these two collections were subjected to considerably different conservation pathways. IPLR-M, which underwent only one propagation cycle, demonstrated marked genetic and phenotypic variability (within and between accessions) in comparison with IPLR-VIR, which had been regularly regenerated over ∼90 years. CONCLUSION: We postulate that long-term ex situ conservation involving human and genotype × environment selection may significantly reduce accession heterogeneity and allelic diversity. Results are further discussed in a broader context of pre-breeding and conservation. © 2019 Society of Chemical Industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".