Bibliographic record
Abstract
Climate change has posed a grim situation in front of us as global temperatures are rising unabated with imminent effect on production and productivity of crops. There is evidence that climate change has already negatively affected wheat and maize yields in many regions. Effect of climate change is also visible on plant-pathogens interactions generally in terms of their higher incidence, more severity and their wider geographical distribution. Pathogens still claim 10–16% of the global harvest. Climate models predict a gradual rise in CO2 concentration and temperature all over the world. Elevated levels of CO2 can directly affect the growth of pathogens. Changes in temperature and precipitation will alter the growth stage, development rate and pathogenicity of the plant pathogens and also physiology and resistance of the host plants. A change in temperature could directly affect the spread of infectious diseases and their survival between the seasons. Published observations on the distribution of 612 crop pests collected over the past 50 years indicate that crop pests have been spreading north and south a little less than 2 miles (3.2 km) a year since 1960. Specifically, losses from stripe rust of wheat were reported from just 11 of the 48 continental US states during the period 1960–1999, whereas during 2000–2014, the disease was reported in a total of 26 states in US and three Canadian provinces. Changing disease scenario with climate change emphasizes the need for developing regular monitoring, forecasting and early warning systems for important host-pathogens which have a direct bearing on the earnings of the farmers and food security at large. Further, adaptation through diversification will be required, which will produce changes in the crop profile that will have effects on disease incidence and severity.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".