Bibliographic record
Abstract
The Biomedical Sciences (BMS) Cluster is one of four key pillars of the Singapore economy. The Singapore Government has injected research funding for basic and translational research to attract companies to carry out their commercial R&D activities. To further intensify the R&D efforts, the National Research Foundation (NRF) was set up to coordinate the research activities of different agencies within the larger national framework and to fund strategic R&D initiatives. In recent years, funding agencies began to focus on support of translational and clinical research, particularly those with potential for commercialization. Translational research is beginning to have traction, in particular research funding for the development of innovation medical devices. Therefore, the Biomedical Sciences sector is projected to grow which means that there is a need to invest in human capital development to achieve sustainable growth. In support of this, education and training programs to strengthen the manpower capabilities for the Biomedical Sciences industry have been developed. In recent years, undergraduate and graduate degree courses in biomedical engineering/bioengineering have been developing at a rapid rate. The goal is to train students with skills to understand complex issues of biomedicine and to develop and implement of advanced technological applications to these problems. There are a variety of career opportunities open to graduates in biomedical engineering, however regardless of the type of career choices, students must not only focus on achieving good grades. They have to develop their marketability to employers through internships, overseas exchange programs, and involvement in leadership-type activities. Furthermore, curriculum has to be developed with biomedical innovation in mind and ensure relevance to the industry. The objective of this paper is to present the NUS Bioengineering undergraduate program in relation to manpower development for the biomedical industry in Singapore.
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) | 0.001 | 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 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".