A Case Study – The Unique Operating Model of the Research Core Facility (RCF) at the Keenan Research Centre (KRC) in the Li-Ka Shing Knowledge Institute (LKSKI) at St. Michael's Hospital, Toronto, Canada.
Bibliographic record
Abstract
For the past several years we have planned a new building to house our Research programme. We used this as an opportnunity to create centralized core facilities with a unique operating model. Here we describe the design process, operating model and highlight the correlation between a well designed and proffesionally run core facility that stresses teaching, collaboration, user productivity and satisfaction. The RCF is a multi-user facility supporting basic science research in the new LKSKI. The underlying design principle for the LKSKI was to foster a collaborative research environment where there was a convergence between scientific investigation, education and patient care. The RCF does this both through its physical design which creates an atmosphere of connectivity, as well as through an emphasis on providing training and project design advice with a focus on teaching people to run their own samples rather than running the samples for them. The facility brought together new and donated equipment, centralizing management through a professional staff of trained scientists. It includes both common and specialized facilities such as; BioImaging, Flow cytometry, Molecular Biology, and Histology. By reducing duplication and increasing usage, the facility offers significant savings, creating a critical mass where cutting edge equipment and techniques are available to scientists who couldn't otherwise afford them. The RCF costs are shared by the users and the Institution. Instead of user fees, which can be hard to quantify and administer, the institution requires that all grants include financial support, equivalent to 3% of the operating budget. The conclusion, amongst the majority of researchers is that after a year of occupation and operation, there is a definite improvement in productivity and user satisfaction. A recent survey indicates that 85% of users are more productive, with 83% rating the facilities and operations as good or excellent.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".