Bibliographic record
Abstract
In June 2023, the ARDC ran a survey of all Australian researchers and research support staff to better understand how they use cloud computing services. This report analyses the findings of the survey.<br> <br> The survey was designed to help guide the development of the ARDC Nectar Research Cloud by identifying service gaps and opportunities to enhance its functionality. The survey results will also contribute to the National Digital Research Infrastructure Strategy, which stems from the 2021 National Research Infrastructure Roadmap.<br> <br> CONTENTS:<br> 1. Executive Summary<br> 2. Background and Context<br> 3. Discipline Grouping<br> 4. Respondents<br> 5. General Use of Cloud<br> 6. Commercial Cloud<br> 7. ARDC Nectar Research Cloud<br> 8. Nectar Cloud vs. Commercial Cloud<br> 9. Australian Results vs Canadian Results<br> 9.1 Background<br> 9.2 Respondents<br> 9.3 General Use of Cloud<br> 9.4 Commercial Cloud<br> 9.5 Nectar Research Cloud/Alliance Community Cloud<br> 10. Conclusions<br> Appendix A: Survey Details<br> A.1 Response rate<br> A.2 Survey issues<br> <br> Learn more about the ARDC Nectar Research Cloud.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.087 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.000 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.141 |
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; both teacher heads agree on what is shown here.
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".