Did anyone get sick this weekend?
Bibliographic record
Abstract

 Background Recreational water illnesses are not as well known as food borne illnesses in the media. There are several pathogens associated with ingesting surface water including Giardia, Cryptosporidium, and Toxoplasmosis. The use of technology for public health surveillance is also little known to the public and can provide much insight into other illnesses on social media not otherwise reported to public health and medical professionals. Illnesses on social media could represent a portion of unreported cases. These cases could be found on social media as a popular outlet for individual expression. Methods Social media posts were found using a variety of keywords including symptoms of significant waterborne illnesses and terms associated with human and environmental contamination. Social media posts were collected from forums and popular social media platforms such as reddit. The posts were then correlated with beach water quality data for a sampling site as geographically close to a case location as possible. Results Social media and water quality data collected from the Columbia river region were correlated. The correlation coefficient of 0.2335 indicates that there is no correlation between social media posts and beach water quality data. Numerous limitations may have impacted the correlation coefficient. Keywords associated with symptoms were more effective in obtaining quality threads and posts compared to other terms. Conclusions Correlating social media posts to water quality data in the Columbia river region does not provide statistically significant results. Manual gathering of social media data for public health surveillance is found to be inefficient and impractical. Further study is required in order to determine the effectiveness of using social media for public health data gathering. It remains to be seen whether correlating posts about illness on social media to water quality data is an effective method of surveillance for public health.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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; both teacher heads agree on what is shown here.
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".