Dry times: hard lessons from the Canadian drought of 2001 and 2002
Bibliographic record
Abstract
Droughts are one of the world's most significant natural hazards. They have major impacts on the economy, environment, health and society. In 2001 and 2002, many regions within Canada experienced unprecedented drought conditions, or conditions unseen for at least 100 years in some regions. This article draws upon a national assessment of this drought with particular attention to its implications for the agriculture and water sectors, although some attention is also devoted to other sectors. The study's methodology involves a comprehensive inter‐disciplinary, cause–effect integrated framework as a basis to explore the characteristics of drought and the associated biological and physical impacts and socio‐economic consequences. Numerous primary and secondary sources of data were used, including public and semi‐public sources such as Agriculture and Agri‐Food Canada, Environment Canada, Statistics Canada, Crop Insurance Corporations and provincial governments, as well as phone interviews, focus groups, print media surveys and economic modelling. Evidence indicates that the risk of drought is increasing as demands for food and water relentlessly climb and the manifestations of climate change become more apparent. The key to better dealing with drought lies in taking the steps necessary to enhance our adaptive capacity and decrease vulnerability.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".