Comparison of four routinely used methods for assessing root colonization by arbuscular mycorrhizal fungi
Bibliographic record
Abstract
Quantifying the proportion of roots colonized by arbuscular mycorrhizal fungi (AMF) is routine work for researchers conducting AMF studies. However, in practice, the methods are always misused, with their adaptability to different conditions neglected. In this study, four frequently used methods (root segment ±, root segment estimation, grid-line intersect, and magnified intersections) were evaluated and compared. Using the light microscopy based staining technique, we assessed AMF colonization of the roots of five plant species (Trifolium repens Linn., Zea mays Linn., Robinia pseudoacacia Linn., Populus simonii Carr., and Caragana korshinskii Kom.). The results revealed that a root length of at least 150 cm (rather than the usual 30 or 50 cm or 100 to 150 intersections generally used when following these four methods) should be examined to represent a single root sample whatever the method used. All four methods had good reproducibility, even though there was a high level of divergence among the results obtained using the different methods to assess the same root sample. We concluded that when assessing the AMF colonization of roots from the same species, all methods except the root segment ± method can be used; however, when assessing root samples from different species, the root segment estimation and magnified intersections methods give more reliable results. We suggest that the root segment ± method is an effective method for revealing the uniformity of AMF distributed in host roots of a certain length.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".