Bibliographic record
Abstract
Responses to various disasters including the World Trade Center attack, Great Eastern JapanEarthquake, Gulf Oil Spill, Ebola and Zika outbreaks, Lac-Mégantic rail disaster, and recenthurricanes and wildfires have revealed the dire need for improved ability to perform rapid datacollection and research for such events. In 2013, leaders from NIH, CDC, and HHS, noted that“the knowledge that is generated through well-designed, effectively executed research inanticipation of, in the midst of, and after an emergency is critical to our future capacity to betterachieve the overarching goals of preparedness and response: preventing injury, illness,disability, and death and supporting recovery.” While much has been done to improve the life-saving response for public health emergencies, these events have also revealed notable gaps inour ability to develop, coordinate, and implement needed scientific research in response to adisaster. It took 11 months to begin a longitudinal health study of exposed workers after theGulf Oil spill. Such delays adversely affect the ability to identify participants or gather vitalinformation to determine disaster-related risk factors such as resiliency, health outcomesrelated to exposures or other stressors, or efficacy of various response activities. Critical dataare lost if not collected in a timely, systematic, and scientifically rigorous manner throughcoordinated interdisciplinary efforts with multiple stakeholders, including impactedcommunities. In response, the NIH Disaster Research Response Program was created tofacilitate time critical data collection and research into national response and recovery efforts.Together with Canada and Japan, progress is being made to overcome some of the challengesand advance disaster research capabilities including availability of research protocols, IRBreview processes, coordination among government agencies, and engagement of academic,public health, and community stakeholders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".