Editorial: Microorganisms in sustainable and green agriculture: synergistic effect on carbon sequestration and crop productivity
Bibliographic record
Abstract
Since the mid-20th century, chemical fertilizers have been widely used to enhance crop 14 productivity, resulting in soil degradation, water pollution, and environmental harm. Microorganisms 15 play a crucial role in soil nutrient cycling and subsequently influence crop productivity, carbon 16 sequestration, soil fertility, and soil health. Specifically, the rhizosphere microbiome could 17 significantly affect plant health, where Plant Growth-Promoting bacteria (PGPB) have emerged as vital 18 allies in mitigating abiotic stresses, such as salt stress and soil-borne diseases. Therefore, it is 19 imperative to develop sustainable and green agricultural systems that can synergistically boost crop 20 productivity, reduce nutrient losses, promote carbon sequestration, improve soil health, and enhance 21 resilience to climate change.The Frontiers Research Topic, Microorganisms in sustainable and green agriculture: synergistic 23 effect on carbon sequestration and crop productivity, invited contributions in the following areas: (a) 24The composition and structure of the microbial community and its impact on plant nutrient uptake and 25 soil organic carbon sequestration; (b) Mechanisms of plant-microbe interactions and their effects on 26 nutrient cycling and organic matter turnover; (c) The influence of land use and management practices 27 on the structure and function of soil microbiome and its impact on nutrient uptake and soil
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".