An approach to the meso-scale epidemiological behavior of Plasmodiophora brassicae from cruciferous crops under tropical conditions; Supplementary material
Bibliographic record
Abstract
Plasmodiophora brassicae is an obligate parasite and a natural soil inhabitant that causes clubroot, a disease with significant economic impact in plants of the Brassicaceae family. This pathology is conditioned by plant/host interactions, edaphoclimatic variables, and mechanisms of inoculum dispersal. However, the epidemiology of this pathogen is not well understood, thereby limiting its incorporation into integrated disease management strategies (IDM). The objective of this work was to adjust a mesoscale risk and prognostic model of P. brassicae based on edaphoclimatic factors and potential dispersal mechanisms in brassica-producing areas in Colombia. The presence and inoculum density of the pathogen were determined by visual inspection of symptoms and quantification by qPCR of soil samples in a total of 127 plots located in regions with the highest production of species from the Brassicaceae family. In addition, an edaphoclimatic characterization was carried out based on field data and secondary information by web scraping using freely available databases. The forecast models were determined by fitting a Generalized Linear Model (GLM) using the logit and inverse link functions for binomial and gamma distributions, respectively. The meso- and macroscale spatial risk model was developed under point pattern approaches (Kernel density model and ecological niche model (ENM). The different epidemiological analysis approaches used suggest that P. brassicae presents a high risk in areas with host presence and conducive edaphoclimatic characteristics, indicating the need to carry out epidemiological surveillance, reduce the dispersion of infested soil, and implement P. brassicae exclusion methods.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".