Inter-organismal signaling and management of the phytomicrobiome
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
The organisms of the phytomicrobiome use signal compounds to regulate aspects of each other's behavior. Legumes use signals (flavonoids) to regulate rhizobial nod gene expression during establishment of the legume-rhizobia N2-fixation symbiosis. Lipochitooligosaccharides (LCOs) produced by rhizobia act as return signals to the host plant and are recognized by specific lysine motif receptor like kinases, which triggers a signal cascade leading to nodulation of legume roots. LCOs also enhance plant growth, particularly when plants are stressed. Chitooligosaccharides activate plant immune responses, providing enhanced resistance against diseases. Co-inoculation of rhizobia with other plant growth promoting rhizobacteria (PGPR) can improve nodulation and crop growth. PGPR also alleviate plant stress by secreting signal compounds including phytohormones and antibiotics. Thuricin 17, a small bacteriocin produced by a phytomicrobiome member promotes plant growth. Lumichrome synthesized by soil rhizobacteria function as stress-sensing cues. Inter-organismal signaling can be used to manage/engineer the phytomicrobiome to enhance crop productivity, particularly in the face of stress. Stressful conditions are likely to become more frequent and more severe because of climate change.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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