Bibliographic record
Abstract
The shared computing and communication infrastructure, known as cloud computing, is supporting a growing number of companies to drive their core businesses. The Cloud term characterizes the end-users perspective: it offers services the users access as outsiders (which could be in the form of a computing and communication platform or infrastructure or an application) while being agnostic about the technology underlying it. The implementation details are abstracted away, and the service/computing is consumed as a pay-per-use service and not acquired as an asset. From the service-provider's perspective, a number of technologies can be deployed to deliver the end-user experience. When the provider is outside of the end user's organization, it is called the public cloud or just the cloud. The same underlying technology can be used to provide similar infrastructure / platforms / software within the organization, perhaps offered by a separate business unit or to take advantage of the benefits while maintaining control; in this case, the term private cloud is used. Separate clouds (separated by technology or management or geography) unified to appear as one are termed federated clouds. When the federated clouds are running different technologies, and in particular do not natively expose same APIs, a more specialized term is a heterogeneous federated cloud. When the clouds being federated are composed of both private and public clouds, the result is a hybrid cloud. Cloud offerings are often classified into three main -as-a-Service (-aaS) categories: Infrastructure-,Platform-, and Software-. Other categories are sometimes used to describe specific implementations of these categories Storage-aaS, Management-aaS, etc.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.151 | 0.099 |
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".