NDRIO White Paper: Envisioning Digital Research Infrastructure for the Simons Observatory
Bibliographic record
Abstract
Observations of the cosmic microwave background (CMB) are an incredibly fertile source of information for studying the origins and evolution of the Universe. Canadian digital research infrastructure (DRI) has played a key role in reducing ever-larger quantities of raw data into maps of the CMB suitable for scientific analysis, as exemplified by the many scientific results produced by the Atacama Cosmology Telescope (ACT) over the past decade. The Simons Observatory (SO), due to start observing in 2023, will be able to measure the CMB with about an order of magnitude more sensitivity than ACT and other current telescopes. In this White Paper we outline how Canadian DRI under the New Digital Research Infrastructure Organization (NDRIO) could build upon the legacy of ACT and play a pivotal role in processing SO data, helping to produce data products that will be central to the cosmology community for years to come. We include estimates of DRI resources required for this work to indicate what kind of advanced research computing (ARC) would best support an SO-like project. Finally, we comment on how ARC allocations could be structured for large collaborations like SO and propose a research data management (RDM) system that makes public data releases available not only for download but also for direct analysis on Canadian DRI.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".