Navigating the labyrinth: a guide to sequence‐based, community ecology of arbuscular mycorrhizal fungi
Bibliographic record
Abstract
Data generated from next generation sequencing (NGS) will soon comprise the majority of information about arbuscular mycorrhizal fungal (AMF) communities. Although these approaches give deeper insight, analysing NGS data involves decisions that can significantly affect results and conclusions. This is particularly true for AMF community studies, because much remains to be known about their basic biology and genetics. During a workshop in 2013, representatives from seven research groups using NGS for AMF community ecology gathered to discuss common challenges and directions for future research. Our goal was to improve the quality and accessibility of NGS data for the AMF research community. Discussions spanned sampling design, sample preservation, sequencing, bioinformatics and data archiving. With concrete examples we demonstrated how different approaches can significantly alter analysis outcomes. Failure to consider the consequences of these decisions may compound bias introduced at each step along the workflow. The products of these discussions have been summarized in this paper in order to serve as a guide for any researcher undertaking NGS sequencing of AMF communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".