Enhanced training through the NSERC CREATE program on New Technologies for Canadian Observatories (NTCO)
Bibliographic record
Abstract
The NSERC CREATE training program on New Technologies for Canadian Observatories (NTCO) has been a unique collaboration between academia, government, and industry to advance innovation in astronomical instrumentation while fostering knowledge exchange as part of an advanced student training program. Through strategic partnerships and funding support, NTCO facilitated the creation of industrial internship opportunities for graduate and undergraduate students in physics, astronomy, and engineering, enabling them to gain valuable professional experience while making high impact contributions to cutting-edge research projects. The NTCO program included nearly 200 supervisors (a third in industry) working together to successfully bridge the gaps between academia, government, and industry, through 70 industrial internships (37 graduate, 33 undergraduate) over the seven-year duration of the program. This paper will outline the key activities and outcomes of the NTCO program, ranging from our strategies in recruiting a diverse group of students and matching them with appropriate industrial internship experiences, to the benefits of advanced summer school training, peer support, annual general meetings, and professional skills development courses for our participants.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".