Bibliographic record
Abstract
The elderly population in Singapore is increasing and is expected to rise to a quarter of the \nSingapore population by 2030. This has raised an alarming concern within the nation and \ncommunity as we have to learn and equip ourselves with the necessary knowledge and skills \nto take care and look out for this vulnerable age group. \n \nOn top of this, the percentage of elderly living in isolation and developing sense of \ndepression is also on a rise. This is especially concerning, especially with the current Covid- \n19 situation, where due to the rules and regulations, there are fewer physical house visits. \nThus, if the elderly encounters a situation such as falling, no one will know. \n \nDespite the government efforts to promote various useful application to the public, the elderly \npopulation may not be receptive to it due to the lack of knowledge on mobile application. The \nlarge influx of information may have an adverse effect on the elderly as they would be more \nconfused, diminishing the intended outcome of these applications. \n \nThus, this mobile application aims to develop a mobile application that include assisted living \nfunctions for the elderly, such as directory of hotlines, to-do-list, fall sensor etc. Even the \naesthetic part of the mobile application is taken into consideration to fit the elderly needs. \nHopefully, this provides the elderlies with the necessary functions to make their day-to-day \nlife easier.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".