A Big Data Management and Analytics Framework for Supporting Machine Learning, OLAP, and Visualization on Big COVID-19 Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Massive amounts of data, including big data, are generated and collected today from a variety of diverse data sources. These big data differ in terms of their veracity that ranges from imprecise and uncertain to precise. These data hide a huge amount of valuable information and precious knowledge that ought to be discovered. Examples of big data in the healthcare and epidemiological fields include information about patients afflicted with diseases such as Coronavirus disease 2019 (COVID-19). Researchers, epidemiologists, and policy makers get a great deal of help from the knowledge discovered from these data via data science techniques such as machine learning, data mining and online analytical processing (OLAP) in order to fully uncover the secrets of the disease. Eventually that may also inspire them to come up with ways to detect, control and fight the disease. In the article, the authors present a machine learning and big data analytical tool useful to process and analyze COVID-19 epidemiological data, while supporting big data visualization and visual analytics.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it