Surveys of nurseries, forests and waterways in Minnesota revealed the presence of 14 new records of <i>Phytophthora</i> species
Bibliographic record
Abstract
Many Phytophthora species are plant pathogens with the capacity to inflict extensive damage and economic losses in nurseries, forest stands and other urban and natural landscapes. This survey aimed to study the Phytophthora species present in Minnesota, USA, and assess the potential damage these Phytophthora could cause to Minnesota’s urban and natural environments. From 2020 to 2023, soil, plant biomass and water were collected from managed and natural forests, nurseries and natural and manmade waterways from 15 counties in Minnesota. Forest sampling was done on stands of trees that had visible signs of stress with some canopy defoliation, canopy chlorosis or tree mortality. Soil and water samples were baited with rhododendron leaves or pears to isolate Phytophthora. Necrotic plant tissue from baits and symptomatic plant tissue collected in the field were cultured on PARPH, a selective medium for Phytophthora. Genetic sequencing was performed to ascertain the presence and specific identities of Phytophthora species. The survey identified 22 Phytophthora species across 15 counties in Minnesota. Of note, 14 species were identified that had not been previously recorded in Minnesota. Several of the species found may have the capability to cause serious damage to woody plants. Additional studies will determine their potential threat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".