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
A promising avenue to control energy-related costs in enterprise data centers is to investigate power-aware resource management strategies. Mechanisms to accurately capture energy consumption in data centers include source and machine code instruction analysis, kernel sensors, system call monitors and per-VM metering techniques. Though very accurate, these approaches are highly invasive, requiring modifications to software or hardware, and introduce an observer effect that can adversely impact performance. Perhaps most important, results obtained from these approaches require refinement before they can actually be used for management decisions that must strike a balance between costs, SLOs and SLAs. Using existing instrumentation at a rack's PDU provides sufficient granularity to determine the true energy consumption of servers in a non-intrusive way. We show that by leveraging existing instrumentation at a rack's PDU, profiling the type of resource (e.g., CPU, memory, disk, network) a process is using on a given server is not only possible, but highly accurate despite the anticipated signal noise from other servers on a rack's power circuit. This provides a better foundation and allows us to forecast and manage energy demands in data centers.
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