Bibliographic record
Abstract
In this thesis we investigate a novel approach to creating tools for the study and implementation of garbage collectors (GC) in contexts ranging from hand-held computers to the Web. We define interactions between GC entities and the global strategy that uses them, e.g. distributed garbage collection (DGC) algorithms. In this setting, we define the notion of a generic collector to be the representation of the ideal collection entity from the global strategy point of view. This definition helps create several tools to improve the way people work with uniprocessor and distributed garbage collectors. This work has led us to consider a new heap organization for uniprocessor GCs. We have designed a Localized Tracing Scheme to optimize GC tracing process at caching and paging levels with possible applications to platforms with limited resources such as hand-held computers. We provide experimental results and show how this organization naturally applies to a parallel setting. To help implement DGCs, we propose a design method based on generic collectors. This method uses models to list important characteristics of each entity in a system. Our methodology explains how to use these models to create local collectors adapted to a particular DGC. This process also allows the design of heterogeneous systems, where each node can choose its own GC. Finally, we use this method to design and implement a garbage collection mechanism for the World Wide Web. After exploring the mapping of memory management paradigms onto concepts of the Web environment, we show that DGC algorithms can be used to detect garbage in websites and avoid dangling links on webpages. We observe that web authors are currently managing web objects explicitly, and an automatic solution should be beneficial. We report on practical implementations and experiments in this web setting. This study leads to the creation of a Web-based platform for experiments in garbage collection research.
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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 | 0.001 |
| 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 | 0.000 | 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 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".