Lessons Learned from the 2017 Flash Drought across the U.S. Northern Great Plains and Canadian Prairies
Bibliographic record
Abstract
Abstract The 2017 flash drought arrived without early warning and devastated the U.S. northern Great Plains region comprising Montana, North Dakota, and South Dakota and the adjacent Canadian Prairies. The drought led to agricultural production losses exceeding $2.6 billion in the United States, widespread wildfires, poor air quality, damaged ecosystems, and degraded mental health. These effects motivated a multiagency collaboration among academic, tribal, state, and federal partners to evaluate drought early warning systems, coordination efforts, communication, and management practices with the goal of improving resilience and response to future droughts. This essay provides an overview on the causes, predictability, and historical context of the drought, the impacts of the drought, opportunities for drought early warning, and an inventory of lessons learned. Key lessons learned include the following: 1) building partnerships during nondrought periods helps ensure that proper relationships are in place for a coordinated and effective drought response; 2) drought information providers must improve their understanding of the annual decision cycles of all relevant sectors, including, and beyond, direct impacts in agricultural sectors; and 3) ongoing monitoring of environmental conditions is vital to drought early warning, given that seasonal forecasts lack skill over the northern Great Plains.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".