Effectors, Effectors <i>et encore des</i> Effectors: The XIV International Congress on Molecular-Plant Microbe Interactions, Quebec
Bibliographic record
Abstract
of this journal, gave a stirring "call to action" opening address the first night.Dr. Sequeira expressed his satisfaction with the growth and quality of the scientific research presented over the years at the IS-MPMI Congresses and in the journal but reminded attendees of the need to enhance the impact and societal relevance of IS-MPMI by "translating" that research into benefits for humankind.His call for translational research became one of the recurring themes of the meeting, as other speakers responded to Dr. Sequeira's comments in their own presentations over the next four days.The importance of charitable organizations that foster translational research for agricultural development and food security, such as the Two Blades Foundation and the Bill and Melinda Gates Foundation, were mentioned by others as emerging examples of
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".