Federated Climate Research Software: improving data and workflow management for climate researchers
Bibliographic record
Abstract
Climate researchers have access to astronomical amounts of data; but finding that data and downloading it so that it can be useful for research can be burdensome and expensive.The team at Data Analytics for Canadian Climate Services (DACCS) is solving that problem by creating a new system for conducting climate research and providing the software to support it. The system works by providing researchers the tools to analyze the data where it’s hosted, eliminating the need to download the data at all.In order to accomplish this, the DACCS team has developed a software stack that includes the following services:- data hosting- data serving (using OPeNDAP protocols)- data search and cataloging- interactive computational environments preloaded with climate analysis tools- remote analysis tools (WPS and OGCAPI features)Partner organizations can deploy this software stack and choose to host any data that they wish. This data then becomes available to every other participating organization, allowing them seamless access each others data without having to move it for analysis.This system will allow researchers to more easily:- discover available data hosted all over the world- develop analysis workflows that can be run anywhere- share their work with collaborators without having to directly share dataThe DACCS team is currently participating in the Open Science Persistent Demonstrator (OSPD) initiative and we hope that this software will contribute to the ecosystem of earth science software platforms available today.
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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 | 0.063 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".