Replication Data for: Ecogeography and utility to plant breeding of the crop wild relatives of sunflower (Helianthus annuus L.)
Bibliographic record
Abstract
Crop wild relatives (CWR) are a rich source of genetic diversity for crop improvement. Combining ecogeographic and phylogenetic techniques can inform both conservation and breeding. Geographic occurrence, bioclimatic, and biophysical data were used to predict species distributions, range overlap and niche occupancy in 36 taxa closely related to sunflower (Helianthus annuus L.). Taxa lacking comprehensive ex situ conservation were identified. The predicted distributions for 36 Helianthus taxa identified substantial range overlap and asymmetry and niche conservatism. Specific taxa (e.g., Helianthus deblis Nutt., Helianthus anomalus Blake, and Helianthus divaricatus L.) were identified as targets for traits of interest, particularly for abiotic stress tolerance and adaptation to extreme soil properties. The combination of techniques demonstrates the potential for publicly available ecogeographic and phylogenetic data to facilitate the identification of possible sources of abiotic stress traits for plant breeding programs. Much of the primary genepool (wild H. annuus) occurs in extreme environments indicating that introgression of targeted traits may be relatively straightforward. Sister taxa in Helianthus have greater range overlap than more distantly related taxa within the genus. This adds to a growing body of literature suggesting that in plants (unlike some animal groups), geographic isolation may not be necessary for speciation.
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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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.438 | 0.245 |
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".