A Decade of Hidden Phytoplasmas Unveiled Through Citizen Science
Bibliographic record
Abstract
Climate change is impacting agriculture in many ways, and a contribution from all is required to reduce the imminent losses related to it. Recently, it has been shown that citizen science could be a way to trace the impact of climate change. However, how can citizen science be applied in plant pathology? Here, using as an example a decade of phytoplasma-related diseases reported by growers, agronomists, and citizens in general, and confirmed by a government laboratory, we explored how to better value plant pathogen monitoring data. Through this collaboration, we found that in the last decade, 34 hosts have been affected by phytoplasmas; 9, 13, and 5 of these plants were, for the first time, reported phytoplasma hosts in eastern Canada, all of Canada, and worldwide, respectively. Another finding of great impact is the first report of a ‘Candidatus Phytoplasma phoenicium’-related strain in Canada, while ‘Ca. P. pruni’ and ‘Ca. P. pyri’ were reported for the first time in eastern Canada. These findings will have a great impact on the management of phytoplasmas and their insect vectors. Using these insect-vectored bacterial pathogens, we show the need for new strategies that can allow fast and accurate communication between concerned citizens and those institutions confirming their observations. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".