How can OpenShift accelerate your Kubernetes adoption: a workshop exploring OpenShift features
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
OpenShift is a Kubernetes distribution which comes with additional capabilities for developers and operators to make building and running cloud-native applications easier. This hands-on workshop walked the participants through the deployment of an application in an OpenShift cluster. Origin Community Distribution of OpenShift (referred to as OKD) is the Kubernetes distribution that powers OpenShift. With Minishift version 3.x, participants can use OKD to get some hands-on experience of OpenShift locally. In addition to managed Kubernetes clusters, IBM Cloud also offers managed OpenShift clusters which leverages a lot of the same infrastructure as the Kubernetes clusters. We also demonstrated how to deploy the application into a paid IBM Cloud OpenShift cluster. We highlighted the similarities and differences between tasks performed in Kubernetes and OpenShift to illustrate the value of using OpenShift. We compared Kubernetes and OpenShift through real example configuration, setup and deployment.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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