RETRACTED: Beyond correlation: Understanding the causal link between microbiome and plant health
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Understanding the causal link between the microbiome and plant health is crucial for the future of crop production. Established studies have shown a symbiotic relationship between microbes and plants, reshaping our knowledge of plant microbiomes' role in health and disease. Addressing confounding factors in microbiome study is essential, as standardization enables precise identification of microbiome features that influence outcomes. The microbiome significantly impacts plant development, necessitating holistic investigation for maintaining plant health. Mechanistic studies have deepened our understanding of microbiome structure and function related to plant health, though much research still needs to be carried out. This review, therefore, discusses current challenges and proposes advancing studies from correlation to causation and translation. We explore current knowledge on the microbiome and plant health, emphasizing multi-omics approaches and hypothesis-driven research. Future studies should focus on developing translational research for producing probiotics and prebiotics from biomarkers that regulate the microbiome-plant health connection, promoting sustainable crop production through microbiome applications.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".