Bibliographic record
Abstract
The Early Warning Infectious Disease Surveillance program (EWIDS) is part of the Cooperative Agreement on Public Health Preparedness and Response for Bioterrorism administered by the Centers for Disease Control and Prevention (CDC). The purpose of EWIDS is to develop and implement a program to collaborate with states or provinces across international borders, to provide rapid and effective laboratory confirmation, and to expand surveillance capabilities. Prior to September 11, 2001, funds were not allocated to states for improving cross-border epidemiologic and laboratory surveillance activities that would increase cross-border preparedness. States were required through the Cooperative Agreement to self-report data twice a year in progress reports to the Division of State and Local Readiness Management Information System (MIS). An analysis of self-reported activities was conducted to determine the activities that states most frequently chose to implement based on existing public health infrastructure along the U.S. borders, since analysis of preparedness activities on the border has not previously been conducted. This article discusses how states chose to address expanding infrastructure capacity with the EWIDS supplemental funding, the challenges that have prevented U.S. border states from addressing all suggested activities, and the importance of sustained funding for the investment of continued capacity building and collaboration with international partners.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".