Chemical genetics and strigolactone perception
Bibliographic record
Abstract
Strigolactones (SLs) are a collection of related small molecules that act as hormones in plant growth and development. Intriguingly, SLs also act as ecological communicators between plants and mycorrhizal fungi and between host plants and a collection of parasitic plant species. In the case of mycorrhizal fungi, SLs exude into the soil from host roots to attract fungal hyphae for a beneficial interaction. In the case of parasitic plants, however, root-exuded SLs cause dormant parasitic plant seeds to germinate, thereby allowing the resulting seedling to infect the host and withdraw nutrients. Because a laboratory-friendly model does not exist for parasitic plants, researchers are currently using information gleaned from model plants like Arabidopsis in combination with the chemical probes developed through chemical genetics to understand SL perception of parasitic plants. This work first shows that understanding SL signaling is useful in developing chemical probes that perturb SL perception. Second, it indicates that the chemical space available to probe SL signaling in both model and parasitic plants is sizeable. Because these parasitic pests represent a major concern for food insecurity in the developing world, there is great need for chemical approaches to uncover novel lead compounds that perturb parasitic plant infections.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".