Bibliographic record
Abstract
A fter my last article for Java Report (“Tapping the Power of JavaScript,” March 1997), I received a question from a reader asking if there was some way for a Java applet to call a JavaScript function. I knew that JavaScript could call Java funtions, but I wasn't sure if things would work the other way around. So, it was off to the Internet to investigate! I wound up at Netscape's official JavaScript documentation site and here's what I found.… THE BASICS First, let's answer the question that started all this: Yes—Java methods can call JavaScript code, and JavaScript code can call Java methods. However, as you might expect, there are some exceptions and restrictions, and those depend on which browser you are using (we'll discuss these a bit later on). The second question you might have is: “Why on earth would you want to do this?” Well, contrary to popular belief, JavaScript isn't just “Java lite,” it's a powerful and useful language in its own right. However, because it's intended to execute inside a Web browser, it does have a few things missing from it. For example, in JavaScript, you have no way to play a sound or access a database. Java has both of these capabilities, and by making them available to JavaScript, we greatly enhance the types of tasks it can perform.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".