Bibliographic record
Abstract
Industrial research projects and collaborations are a key component to any applied research program and can provide unique opportunities for industry, academic institutions and students. Efforts, in particular for small and medium sized companies, to improve and advance commercial opportunities in the natural products and food industries are advanced through partnerships with researchers at local universities. These collaborations can lead to innovations, expanded market share and ultimately increase revenues. There are many partnership funding programs that have increased industry access to researchers and students, thereby opening to door to technology access for scientifically designed sampling and testing that would otherwise be too costly for the individual company. By partnering to establish proof of concept before the company makes a significant financial commitment, minimizes risk for the company and ultimately paves the path for success. The benefits also extend to the researchers and students involved in the project who engage in advancing the state of practice and gain experience applying their expertise to an industrial setting. Our research program at BCIT has engaged in several federally funded projects including NSERC Engage and CUI2I grants allowing us to establish new partnerships with minimal financial risk to the industry partners. This talk will discuss the process involved in developing these partnerships, granting opportunities, research outcomes and student engagement using real examples of research partnerships that we have engaged in the last few years.
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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".