Docker containers made fun: Let it run!
Bibliographic record
Abstract
Motivation: I was tired of seeing VM vs Docker comparison explained by stacked rectangular boxes to introduce containerization concept. 1) The cover of the presentation hints some Docker features in Frozen (movie) theme: <br> <br> Docker can run frozen environments inside lightweight boxes isolated from your host machine. When you have many containers running, it becomes a "kingdom of isolation". So Docker basically "lets you run". <strong>It is a kingdom of isolation, and looks like you are coding.</strong> <strong>Let it run, let it run, I can't debug it anymore. </strong> 2) I used a concrete buildings vs spartan container houses analogy to explain the differences between VMs and containers. I came up with real-estate commercials, highlighting powerful aspects of both.<br> <br> Finally, I briefly introduce how "science-oriented" containers are different than the "market-oriented" ones. I have another presentation drilling down on this: https://zenodo.org/record/3625531#.XirCpFNKhsM
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.016 |
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; both teacher heads agree on what is shown here.
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".