Bibliographic record
Abstract
J ava and corba fit together. With Java, you have portability of code and platform independence. With CORBA you add location transparency and an enterprise level object model that allows us to interoperate with a multitude of existing languages and integrated or legacy systems. One of the most important steps when designing your client applications and applets is how they should bootstrap into the CORBA system. With a good system design, you can make this bootstrapping phase straightforward and avoid any bottlenecks along the way. You need to consider how CORBA servers should distribute CORBA object references so that clients can easily and efficiently find them. Some of your decisions may be made at the relatively early IDL design phase, while others can be implemented as late as when you deploy your clients and servers. BOOTSTRAPPING A CORBA APPLICATION A CORBA application only needs to obtain one CORBA Object reference (otherwise known as an Interoperable Object Reference (IOR)) for it to be able to connect to and participate in a CORBA system. From then on, a CORBA client or server should be able to obtain new IORs through normal IDL invocations. Therefore, it is the mechanism by which a client or server obtains this initial object reference that can be vital for a CORBA system's overall accessibility and scalability. The most interoperable and scalable solution to locating CORBA objects is to use the CORBA Naming Service.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.035 |
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".