RocTest: A standardized method to assess the performance of root organ cultures in the propagation of arbuscular mycorrhizal fungi
Bibliographic record
Abstract
Over the past three decades, root organ cultures (ROCs) have been the gold standard method for studying arbuscular mycorrhizal fungi (AMF) under in vitro conditions, and ROCs derived from various plant species have been used as hosts for AM monoxenic cultures. While there is compelling evidence that host identity can significantly modify AMF fitness, there is currently no standardized methodology to assess the performance of ROCs in the propagation of their fungal symbionts. We describe RocTest, a robust methodological approach that models the propagation of AMF in symbiosis with ROCs. The development of extraradical fungal structures and the pattern of sporulation are modeled using cumulative link mixed models and linear mixed models. We demonstrate functionality of RocTest by evaluating the performance of three species of ROCs (Daucus carota, Medicago truncatula, Nicotiana benthamiana) in the propagation of three species of AMF (Rhizophagus clarus, Rhizophagus irregularis, Glomus sp.). RocTest produces a simple graphical output to assess the performance of ROCs and shows that fungal propagation depends on the three-way interaction between ROC, AMF, and time. RocTest makes it possible to identify the best combination of host/AMF for fungal development and spore production, making it an important asset for germplasm collections and AMF research.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".