Comment analyser la structure spatiale et modéliser le développement spatio-temporel des épiphyties?
Bibliographic record
Abstract
The spatial dimension of plant disease development has often been neglected by most epidemiologists, despite its importance. Yet several tools are available for analysis and modeling of epidemics, both in time and space. Methods for spatial analysis include clustering index calculation, distribution fitting, power law, relationships between incidences at different spatial scales, mapping, geostatistics, and distance indices with SADIE software. The tools for spatio-temporal modeling include spatially explicit or spatially implicit models. Among the spatially explicit models, we find reaction-diffusion, network, or individual-based models, cellular automata, and lattice models, including some metapopulation models. Spatially implicit models are based on the introduction of a correction factor, the percolation theory, or statistical approximations. This review presents a rough guide to spatio-temporal approaches, in the hope that their use will become widespread in the community of epidemiologists, even among nonspecialists in spatial information. The tools that can be used to analyze and model the spatial structure of epidemics are reviewed in the context of their application to phytopathology. The diffusion of this information should promote a better understanding of epidemics and the design of innovative management strategies.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".