The MVM Ventilator: Particle physicists, National Labs and Industry
Bibliographic record
Abstract
In response to the needs for ventilators for critically ill Covid-19 patients a collaboration of international particle physicists, engineers, software specialists, industry and medical specialists came together to create rapidly a simple, low-cost, open-source ventilator tailored specifically for such patients. The project was initiated by Professor Cristian Galbiati, spokesperson of the DarkSide-20k experiment designed to use about 55 tons of low-radioactivity liquid Argon to search for high-mass WIMPS as Dark Matter candidate particles at the Gran Sasso underground laboratory. This experiment is a successor to the DEAP experiment and pre-cursor to the proposed ARGO experiment at SNOLAB. It was realized that the expertise in gas handling and computer control that has been developed for these experiments could be used to develop the Mechanical Ventilator Milano (MVM). The Italian group was immediately joined by Canadian scientists and engineers from TRIUMF laboratory, CNL Chalk River, SNOLAB and the McDonald Institute and other international collaborators. A prototype was working in the lab within 10 days, papers were published openly for the design, industrialized versions were developed and the collaboration won a Canadian government contract that has now resulted in the delivery of over 6000 ventilators to the Canadian stockpile following Health Canada Interim Authorization. Donation to other countries in need is another possibility under discussion. A description will be provided of how this highly motivated team pivoted their work to make a contribution to the COVID-19 pandemic.
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 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".