Rising Star in Dependability Award
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Starting from 2020, a new award called Rising Star in Dependability Award is presented annually at the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) to recognize a junior researcher, from academia or industry, who demonstrates outstanding potential for creative ideas and innovative research in the field of dependable and resilient computer systems and networks. The award is jointly sponsored by the IEEE TC on Dependable Computing and Fault Tolerance (TCFT) and IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance (WG 10.4). To be eligible, a candidate must have graduated no more than 10 years before the nomination deadline (considering the year as a reference). Career disruptions or delays (e.g. Parental leaves) that may have been experienced by the candidates are taken into consideration by the selection committee. A candidate may be nominated a maximum of two times. Previous recipients of the Award are not eligible. Self nominations and nominations by the Rising Star award committee members are not allowed The Rising star in dependability award is selected by an Award Committee appointed by the IEEE TCFT Chair, the IFIP WG 10.4 Chair and the current DSN PC chairs. The award takes the form of a plaque presented to the award recipient at the conference. The award recipient is required to attend DSN to receive the award and is invited to give a presentation to DSN attendees. His/her conference registration is borne by the conference. The winner of the 2020 Rising Star in Dependability Award is: Karthik Pattabiraman (University of British Columbia, Canada).
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it