PlanetIgnite: A Self-Assembling, Lightweight, Infrastructure-as-a-Service Edge Cloud.
Bibliographic record
Abstract
PlanetIgnite is a general-purpose, Infrastructure-as-a-Service, self-assembling, lightweight edge cloud on virtualized infrastructure with support for single-pane-of-glass distributed application configuration and deployment. This is an entirely new concept. PlanetLab[32], GENI[7], [22], and SAVI[19] are general-purpose IaaS edge clouds, but require top-down installation and dedicated hardware resources at each site and do not offer single-pane-of-glass application deployment. Seattle[11] is a lightweight self-assembling edge cloud that offers single-pane-of-class configuration and control, but developers are restricted to using a subset of Python. PlanetIgnite is a Containers-as-a-Service Edge Cloud which offers Docker Containers to each PlanetIgnite user. A PlanetIgnite node is an off-the-shelf Ubuntu 14.04 Virtual machine with Docker installed, meaning it can be installed on any edge node where a VM with a routable v4 address is available. Adding a PlanetIgnite node to the infrastructure is simple: a site wishing to host a PlanetIgnite node simply downloads the image, on boot, the new PlanetIgnite node registers with the PlanetIgnite portal, which runs a series of acceptance tests. Once complete, the image is registered and the node is added to the set of PlanetIgnite sites.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".