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
Many websites with a large user base, e.g., websites of nonprofit organizations, do not have the financial means to install large web-servers or use specialized content distribution networks such as Akamai. For those websites, we have developed Flower-CDN, a locality-aware P2P based content-distribution network (CDN) in which the users that are interested in a website support the distribution of its content. The idea is that peers keep the content they retrieve and later serve it to other peers that are close to them in locality. Our architecture is a hybrid between structured and unstructured networks. When a new client requests some content from a website, a locality-aware DHT quickly finds a peer in its neighborhood that has the content available. Additionally, all peers in a given locality that maintain content of a particular website build an unstructured content overlay. Within this overlay, peers gossip information about their content allowing the system to maintain accurate information despite churn. In our performance evaluation, we compare Flower-CDN with an existing P2P-CDN strictly based on DHT and not locality aware. Flower-CDN reduces lookup latency by a factor of 9 and transfer distance by a factor of 2. We also show that Flower-CDN's gossip has low overhead and can be adjusted according to hit ratio requirements and bandwidth availability.
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