Does Commercial Inoculation Promote Arbuscular Mycorrhizal Fungi Invasion?
Bibliographic record
Abstract
Interventions with commercial inoculants have the potential to reduce the environmental footprint of agriculture, but their indiscriminate deployment has raised questions on the unintended consequences of microbial invasion. In the absence of explicit empirical reports on arbuscular mycorrhizal fungi (AMF) invasion, we examine the present framework used to define AMF invasion and offer perspectives on the steps needed to avoid the negative impacts of AMF invasion. Although commercial AMF isolates are potential invaders, invasions do not always constitute negative impacts on native community diversity and functions. Instead, the fates of the invading and resident communities are determined by ecological processes such as selection, drift, dispersal, and speciation. Nevertheless, we recommend strategies that reduce overdependence on introduced inoculants, such as adoption management practices that promote the diversity and richness of indigenous AMF communities, and the development of native propagules as a supplement to commercial AMF in applicable areas. Policies and regulations that monitor inoculant value chains from production to application must be put in place to check inoculant quality and composition, as well as the transport of inoculants between geographically distant regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".