Shifts in the abiotic and biotic environment of cultivated sunflower under future climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sunflower is a unique model species for assessing crop responses and adaptation to climate change. We provide an initial assessment of how climate change may influence the abiotic and biotic environment of cultivated sunflower across the world. We find an 8% shift between current and future climate space in cultivated sunflower locations globally, and a 48% shift in Northern America, where the crop originates. Globally, the current niche occupied by sunflower crop wild relatives offer few opportunities to adapt to future climate for cultivated sunflower, but in Northern America 100% of the future climate space of cultivated sunflower is filled by the niche of primary wild relative germplasm alone ( e.g. wild Helianthus annuus ). Globally, we find little difference in the overlap between current and future climate space of cultivated sunflower with the niche of the important sunflower pathogen Sclerotinia sclerotiorum , but in Northern America, climate change will decrease the overlap between local populations of this pest and cultivated sunflower by 38%. Our analysis highlights the utility of multi-scale analysis for identifying candidate taxa for breeding efforts and for understanding how future climate will shift the abiotic and biotic environment of cultivated crops.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it