Bibliographic record
Abstract
The subtle changes in climate attributed to climate change can affect plant-disease development. These changes are not easily determined, and consequently, the ability to forecast how disease changes under altered growth conditions is not simple. One method is the use of forecast climate change derived from global-change models that are analogous to general-circulation models used for weather forecasts. However, these models predict conditions on such a gross scale that they are unacceptable for most disease forecasting. Downscaling provides a method whereby weather and climate conditions estimated at a very large scale can be transferred to a fine resolution (∼200-m grid points). This process is explained, in particular, in the context of disease forecasting. An example is presented of how estimates of extreme low temperature at a local scale have been derived from a mesoscale (mid-range) weather forecast model, which in turn was derived from a general-circulation model. Similarly, the derivation of forecasts at local scale from mesoscale weather forecast models have been demonstrated for grapevine downy mildew. Important considerations of scale definition and information transfer across different scales are discussed
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".