A Model-Based Approach for Crawling Rich Internet Applications
Why this work is in the frame
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Bibliographic record
Abstract
New Web technologies, like AJAX, result in more responsive and interactive Web applications, sometimes called Rich Internet Applications (RIAs). Crawling techniques developed for traditional Web applications are not sufficient for crawling RIAs. The inability to crawl RIAs is a problem that needs to be addressed for at least making RIAs searchable and testable. We present a new methodology, called “model-based crawling”, that can be used as a basis to design efficient crawling strategies for RIAs. We illustrate model-based crawling with a sample strategy, called the “hypercube strategy”. The performances of our model-based crawling strategies are compared against existing standard crawling strategies, including breadth-first, depth-first, and a greedy strategy. Experimental results show that our model-based crawling approach is significantly more efficient than these standard strategies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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