Bibliographic record
Abstract
Today 8% of Canadian babies are born premature and internationally the average is 10% These early births, are responsible for three quarters of all infant deaths in Canada. Premature infants together with ill term infants are cared for in Neonatal Intensive Care U nits (NICUs) internationally contain state of th e art medical equipment to monitor and provide life support, resulting in a significant Big Data environment. In addition, graduates of neonatal intensive care may be discharged with medical devices to support continued monitoring as ambulatory patients in and outside the ho me setting. In both NICU and ambulatory contexts wearable patient monitoring has many social implications. This research presents an assessment of the social implications of Big Data solutions for criti cal care within the context of the Artemis project that is enabling Big Data solutions for: 1) Real-ti me processing of complex intensive care physiological signals for new and earlier condition onset detection; 2) new approaches to physiological data analysis to support clinical research; and 3) cloud computing/services computing to provide rural and remote communities with greater options for a dvanced critical care within their own community healthcare facilities.
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 | 0.000 | 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) | 0.000 | 0.001 |
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".