What are the Top 10 Unanswered Questions in Molecular Plant-Microbe Interactions?
Bibliographic record
Abstract
This article is part of the Top 10 Unanswered Questions in MPMI invited review series. The past few decades have seen major discoveries in the field of molecular plant-microbe interactions. As the result of technological and intellectual advances, we are now able to answer questions at a level of mechanistic detail that we could not have imagined possible 20 years ago. The MPMI Editorial Board felt it was time to take stock and reassess. What big questions remain unanswered? We knew that to identify the fundamental, overarching questions that drive our research, we needed to do this as a community. To reach a diverse audience of people with different backgrounds and perspectives, working in different areas of plant-microbe interactions, we queried the more than 1,400 participants at the 2019 International Congress on Molecular Plant-Microbe Interactions meeting in Glasgow. This group effort resulted in a list of ten, broad-reaching, fundamental questions that influence and inform our research. Here, we introduce these Top 10 unanswered questions, giving context and a brief description of the issues. Each of these questions will be the subject of a detailed review in the coming months. We hope that this process of reflecting on what is known and unknown and identifying the themes that underlie our research will provide a framework to use going forward, giving newcomers a sense of the mystery of the big questions and inspiring new avenues and novel insights. [Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".