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
Named data networking (NDN) is a content-centric future Internet architecture that uses routable content names instead of IP addresses to achieve location-independent forwarding. Nevertheless, NDN's design is limited to offering hosted applications a simple content pull mechanism. As a result, increased complexity is needed in developing applications that require more sophisticated content delivery functionalities (e.g., push, publish/subscribe, streaming, generalized forwarding, and dynamic content naming). In this paper, we introduce a novel Enhanced NDN (ENDN) architecture that offers an extensible catalog of content delivery services (e.g., adaptive forwarding, customized monitoring, and in-network caching control) that can be programmed in the data plane using customizable P4 programs. More precisely, the proposed architecture allows hosted applications to associate their content namespaces with a set of services offered by the ENDN control plane. The controller then configures the data plane, which is comprised of two main modules: the enhanced packet processing and the forwarding logic modules. The former parses the packets and queries the enhanced content-based forwarding tables to generate a set of metadata fields used by P4 functions. The latter module is a novel P4 target architecture that executes these P4 functions on the arriving packets. The new architecture extends existing P4 models to overcome their limitations with respect to processing string-based content names. It also allows running independent P4 functions in isolation, thus enabling P4 code run-time pluggability. Experimental results demonstrate the ability of ENDN to achieve network efficiency with low latency.
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