Overview and Update of the North America Drought Monitor and North America Climate Extremes Monitoring System
Bibliographic record
Abstract
Drought can occur in any part of the world, and its impact can be devastating. Consequently, drought monitoring has become an integral part of drought planning, preparedness, and mitigation efforts in the United States at the national, regional, and local levels (Lawrimore et al., 2002). The U.S. Drought Monitor (USDM) was created in 1999 to track and display the magnitude and spatial extent of drought and its impacts across the nation, and it has been extremely successful in communicating the state of U.S. drought on a weekly basis (Svoboda et al., 2002; Lawrimore et al., 2002). This success, and the recognition that an ongoing comprehensive and integrated drought assessment was needed across North America, led to a commitment between Canada, Mexico, and the United States to build a continent-scale program on the model of the USDM. This new first-of-its-kind drought monitoring program is part of a broader effort to improve the monitoring and assessment of climate extremes across the continent through a cooperative effort that was established in 2001 between the three countries (Lawrimore et al., 2002). This paper will provide an update on the status of the monthly North America Drought Monitor (NADM) and review the procedure involved in its preparation. The progress toward implementing a broader North America Climate Extremes Monitoring (NACEM) program will also be reviewed.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".