Replication Data for: Crop wild relatives of pigeonpea [Cajanus cajan (L.) Millsp.]: Distributions, ex situ conservation status, and potential genetic resources for abiotic stress tolerance
Bibliographic record
Abstract
Pigeonpea [Cajanus cajan (L.) Millsp.] is a versatile, stress-tolerant, and nutritious grain legume, possessing traits of value for enhancing the sustainability of dry sub-tropical and tropical agricultural systems. The use of crop wild relatives (CWR) in pigeonpea breeding has been successful in providing important resistance, quality, and breeding efficiency traits to the crop. Current breeding objectives for pigeonpea include increasing its tolerance to abiotic stresses, including heat, cold, drought, and waterlogging. Here we assess the potential for pigeonpea CWR to be further employed in crop improvement by compiling wild species occurrence and ex situ conservation information, producing geographic distribution models for the species, identifying gaps in the comprehensiveness of current germplasm collections, and using ecogeographic information to identify CWR populations with the potential to contribute agronomic traits of priority to breeders. The fifteen prioritized relatives of pigeonpea generally occur in South and Southeast Asia to Australia, with the highest concentrations of species in southern India and northern Australia. These taxa differ considerably among themselves and in comparison to the crop in their adaptations to temperature, precipitation and edaphic conditions. We find that these wild genetic resources are broadly under-represented in ex situ conservation systems, with 80% of species assessed as high priority for further collecting, thus their availability to plant breeders is insufficient. We identify species and highlight geographic locations for further collecting in order to improve the completeness of pigeonpea CWR germplasm collections, with particular emphasis on potential traits for abiotic stress tolerance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.614 | 0.231 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".