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
The number of publicly accessible virtual execution environments (VEEs) has been growing steadily in the past few years. To be accessible by clients, such VEEs need either a public IPv4 or a public IPv6 address. However, the pool of available public IPv4 addresses is nearly depleted and the low rate of adoption of IPv6 precludes its use. Therefore, what is needed is a way to share precious IPv4 public addresses among a large pool of VEEs. Our insight is that if an IP address is assigned at the time of a client DNS request for the VEE's name, it is possible to share a single public IP address amongst a set of VEEs whose workloads are not network intensive, such as those hosting personal servers or performing data analytics. We investigate several approaches to multiplexing a pool of global IP addresses among a large number of VEEs, and design a system that overcomes the limitations of current approaches. We perform a qualitative and quantitative comparison of these solutions. We find that upon receiving a DNS request from a client, our solution has a latency as low as 1 ms to allocate a public IP address to a VEE, while keeping the size of the required IP address pool close to the minimum possible.
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 | 0.001 | 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 | 0.005 | 0.002 |
| 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