Harnessing phytomicrobiome signaling for rhizosphere microbiome engineering
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 goal of microbiome engineering is to manipulate the microbiome toward a certain type of community that will optimize plant functions of interest. For instance, in crop production the goal is to reduce disease susceptibility, increase nutrient availability increase abiotic stress tolerance and increase crop yields. Various approaches can be devised to engineer the plant-microbiome, but one particularly promising approach is to take advantage of naturally evolved plant-microbiome communication channels. This is, however, very challenging as the understanding of the plant-microbiome communication is still mostly rudimentary and plant-microbiome interactions varies between crops species (and even cultivars), between individual members of the microbiome and with environmental conditions. In each individual case, many aspects of the plant-microorganisms relationship should be thoroughly scrutinized. In this article we summarize some of the existing plant-microbiome engineering studies and point out potential avenues for further research.
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 | 0.000 |
| 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