Bibliographic record
Abstract
Abstract We are in an information era when data are generated in masses: from devices that stream data in a health context, such as wearable fitness devices, to genomics data, to health exposure data from a variety of monitors that may be misaligned, and to earth observation data from satellites. The Big Data era in which we live is viewed as having the power to revolutionize society. The term big data has different meanings to different sectors: for engineers, for example, this term encompasses methods and tools for transmission of data faster, including wireless transmissions; for sociologists, it encompasses curation methods and techniques; for computer scientists, the term encompasses information management and security systems and analytics; and for statisticians, the term principally refers to data analytical techniques. This entry discusses challenges and opportunities in big data in biosciences exemplified through three important big data areas from health, environmental studies, and earth observation: human population genomics, forest fire analytics, and smoke estimation from satellite imagery.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.022 |
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".