Increased growth in sunflower correlates with reduced defences and altered gene expression in response to biotic and abiotic stress
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
Cultivated plants have been selected by humans for increased yield in a relatively benign environment, where nutrient and water resources are often supplemented, and biotic enemy loads are kept artificially low. Agricultural weeds have adapted to this same benign environment as crops and often have high growth and reproductive rates, even though they have not been specifically selected for yield. Considering the competing demands for resources in any plant, a key question is whether adaptation to agricultural environments has been accompanied by life history trade-offs, in which resistance to (largely absent) stress has been lost in favour of growth and reproduction. The experiments reported here were designed to test for growth-defence trade-offs in agricultural weeds, crops and native varieties of common sunflower (Helianthus annuus L., Asteraceae) by comparing their performance in the presence or absence of abiotic (drought and crowding) or biotic (simulated herbivory, insect herbivory and fungal) stress. We found that growth, as well as viability of crops and weeds, was reduced by abiotic drought stress. The weakened defence in the agricultural genotypes was further evident as increased susceptibility to fungal infection and higher level of insect palatability. To uncover molecular mechanisms underlying these trade-offs, we monitored gene expression kinetics in drought-stressed plants. By correlating phenotypic observations with molecular analyses, we report the identification of several genes, including a protein phosphatase 2C and the HD-Zip transcription factor Athb-8, whose expression is associated with the observed phenotypic variation in common sunflower.
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