Using molecular biology to study mycorrhizal fungal community ecology: Limits and perspectives
Bibliographic record
Abstract
Molecular tools have progressively replaced morphological approaches to characterize microbial communities in nature. Arbuscular mycorrhizal (AM) fungi are no exception to this rule. Yet, one challenge posed by these symbionts is that they colonize simultaneously both plant roots and soil, which complicates their detection and quantification. In most studies conducted to date, AM fungal communities have been characterized from roots only, soil only or spores only. Here, we discuss the pitfalls associated to drawing ecological inferences using such datasets. We also conclude by arguing that molecular biology will contribute most to advance knowledge in AM fungal ecology if it is integrated into broader perspectives taking into account the natural history of these organisms. This calls for a better merging of molecular and morphological approaches, and the establishment of intensive, long-term research programs.
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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 | 0.056 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".