Do Bioinoculants Affect Resident Microbial Communities? A Meta-Analysis
Bibliographic record
Abstract
There is a global industry built upon the production of “bioinoculants,” which include both bacteria and fungi. The recent increase in bioinoculant uptake by land users coincides with a drive for more sustainable land use practices. But are bioinoculants sustainable? These microbes are believed to improve plant performance, but knowledge of their effect on resident microbial communities is scant. Without a clear understanding of how they affect soil microbial communities (SMC), their utility is unclear. To assess how different inoculation practices may affect bioinoculant effects on SMC, we surveyed the existing literature. Our results show that bioinoculants significantly affect soil microbial diversity and that these effects are mediated by inoculant type, diversity, and disturbance regime. Further, these changes to soil microbes affect plant outcomes. Knowledge that these products may influence crop performance indirectly through changes to soil microbial diversity attests to the importance of considering the soil microbiome when assessing both bioinoculant efficacy and threats to soil ecosystems.
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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".