Bibliographic record
Abstract
The climate is changing and the evidence is all around.Springs are arriving earlier, summers are longer and hotter, winter temperatures are warmer, and plant hardiness zones have shifted northward.Since 1895, temperature in the United States has increased by an average of 0.72-1.05°C,with most warming occurring in the last 45 years.The decade 2001-2010 was the hottest on record, and the trajectory is set for the average global temperature to continue to warm into the future from 1.8° to 4.0°C by 2100.This anticipated increase in temperature, along with changes in precipitation patterns and extreme events (e.g., heat waves, droughts), will have profound implications for pests (e.g., arthropod, disease, weed) that inhabit agriculture and forestry systems.To protect these systems and their benefits for the environment, economies, and society and culture, we need to understand how climate changes will affect pest distribution, ecology, and virulence, which are already taking a toll (Fig. 1).Only then can resource managers better prepare and adapt their practices.With funding from the USDA National Institute of Food and Agriculture, the National Forum on Climate and Pests (NFCP) was convened at the National Academies in Washington DC on October 4-6, 2016.The NFCP brought together 26 scientists from the climate and pest science disciplines in front of a live Internet audience to provide the latest information on how climate change affects many aspects of pest biology and to discuss ways to create more resilient and productive agriculture and forestry ecosystems.The long-term goal of the NFCP was to bring attention to and strategize ways to develop resilient agricultural and forest ecosystems in the face of climate-induced changes in pest distribution and severity.This goal aligns with the USDA Agriculture and Food Research Initiative's (AFRI) Agriculture and Natural Resources Science for Climate Variability and Change Challenge Area, which is focused on reducing the greenhouse gas footprint of U.S. agriculture and forestry as a whole, while maintaining resilient and productive ecosystems.The NFCP was organized by individuals representing various private and public organizations, including the American Society of Agronomy, an international organization with a membership base that includes
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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".