It takes three to tango: the importance of microbes, host plant, and soil management to elucidate manipulation strategies for the plant microbiome
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 world’s population is expected to grow to almost 10 billion by 2050, placing unprecedented demands on agriculture and natural resources. The risk in food security is also aggravated by climate change and land degradation, which compromise agricultural productivity. In recent years, our understanding of the role of microbial communities on ecosystem functioning, including plant-associated microbes, has advanced considerably. Yet, translating this knowledge into practical agricultural technologies is challenged by the intrinsic complexity of agroecosystems. Here, we review current strategies for plant microbiome manipulation, classifying them into three main pillars: (i) introducing and engineering microbiomes, (ii) breeding and engineering the host plant, and (iii) selecting agricultural practices that enhance resident soil and plant-associated microbial communities. In each of these areas, we analyze current trends in research, as well as research priorities and future perspectives.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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