Bibliographic record
Abstract
On the evening of Monday, March 4, 2024, the Nova Scotian Institute of Science (NSIS) held a student symposium as part of its public lecture series. This year’s theme was Scientific Research in Nova Scotia, welcoming talks from all scientific disciplines.One goal was to highlight the diversity and breadth of research currently being conducted by students across the province. The NSIS Council therefore decided, as part of its lecture series, to invite short, 3-minute talks from students with projects at any stage of com-pletion. The focus was to attract young researchers to contribute more to NSIS activities. The limited time enabled the session to maximize the number of presenters. Many strong applications to present were received, and eight students were selected from various academic levels (from undergraduates to doctoral students) to give talks about their research. They also answered questions from the live audience and those watching and listening online. At the end of the evening, awards were presented to selected contributors, and all students were commended for very well-presented and interesting talks.The abstracts for the talks are provided below.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".