Ärisüsteemide partitsioneerimine Docker platvormil
Bibliographic record
Abstract
Ärisüsteemid on keerulised arvutisüsteemid, mis loovad väärtust nende omanikele. Reegilina süsteemide väärtused koos oma olemustega erinevad üksteisest, kuid süsteemid ise on ehitatud geneerilisematest komponentidest, mis on kokku seotud tegelikku väärtust lisava äriloogikaga. Antud süsteemid võivad olla paigutatud suurtesse riistvaraklastritesse. Sellest tulenevalt võib selliste süsteemide loomine ja haldamine nõuda märgatavalt vaeva.\\n\\rKäesolev lõputöö uurib Dockerit, mis on tarkvara konteinerite platvorm. Seda kasutedes on püütud standardiseerida ning automatiseerida tehisliku ärisüsteemi juurtamist. Ühtlasi fokuseerib antud uurimistöö ärisusteemide partitsioneerimise automatiseerimisele mitmele riistvara sõlmele. Selle saavutamiseks testitakse METIS graafi partitsioneerimise teekide ja tööriistadega.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".