Bibliographic record
Abstract
Extreme weather events have taken a substantial toll on human livelihoods and lives around the globe, and have often detrimentally affected food production and security.In 2005, persistent droughts in several African countries severely limited food supplies, flooding in Bangladesh routinely disrupted agriculture, heat spells in Australia caused crop and livestock losses, and North American hurricanes such as Katrina led to significant crop losses and blocked grain transportation systems.With climate change, the expectation is that temperatures will rise, moisture conditions will change, and many extreme climate events will become more common.Given the effects of recent extreme weather, questions arise about the capacity of agri-food systems to handle changed climate and weather in the future.Such capacity may be found in individuals and families, local communities, regional authorities, business and corporations, and/or national governments.All have a part to play in preparing for challenges -both risks and opportunities -from future climatic and weather conditions.In Canada, indications are that climate change is already having an effect on farming, thereby increasing the need for research and programs to assist adaptive decision making.Several groups in the Canadian agri-food sector seek relevant and timely information.One is industry-related, including producers and agribusiness interests who view climate and weather risks as one of several factors to be considered in operating strategies affecting farm production practices and financial management.Another is made up of policy makers charged with the task of developing programs and legislation that can enhance the agri-food sector's ability to manage climate risks and take advantage of opportunities.A third group is the research community, which seeks to improve the understanding of the implications of climate change for the agri-food sector and to provide a sound basis for making decisions about adaptive strategies.To date, information about climate change impacts and adaptation has for the most part been fragmented, in terms of both the issues focused on
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.658 | 0.461 |
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".