A decade of hidden phytoplasmas unveiled through citizen science
Bibliographic record
Abstract
ABSTRACT Climate change is impacting agriculture in many ways, and a contribution from all is required to reduce the imminent loses related to it. Recently, it has been showed 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, citizens in general, and confirmed by a government laboratory, we explore a new way of valuing plant pathogens monitoring data deriving from land-users or stakeholders. Through this collaboration we found that in the last decade thirty-four hosts have been affected by phytoplasmas, nine, thirteen and five of these plants were, for the first time, reported phytoplasma hosts in Eastern Canada, in Canada and worldwide, respectively. Another finding of great impact is the first report of a ‘ Ca . P. phoenicium’-related strain in Canada, while ‘ Ca . P. pruni’ and ‘ Ca . P. pyri’ was reported for the first time in Eastern Canada. These findings will have a great impact in the management of phytoplasmas and their insect vectors. Using these insect-vectored bacterial pathogens, we show the needs of new strategies that allow a fast and accurate communication between concerned citizens and those institutions confirming their observations. Abstract Figure
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".