Bibliographic record
Abstract
Over the past decade, data mining has gained a strong foothold in a variety of application areas, including including science, engineering, and commerce. Centralized techniques, assuming all data to be contained at a single site, have been successfully applied to a large variety of application domains. However, we currently observe a trend toward a distributed extraction of knowledge from datasets. The motivation for this trend includes the distributed nature of the data itself, the distributed nature of computational resources, privacy concerns, and pragmatic issues arising from the increasing amount of available data. The development of distributed data-mining techniques is therefore necessary to extend the success of centralized data-mining techniques to the distributed domain. We present our research in the area of distributed data-mining. Specifically, we introduce a light-weight technique, in which predictive and descriptive models are built locally from subsets of attributes, and combined to produce global descriptive or predictive results. We modify our technique to facilitate data-mining in sensor networks for static and dynamic data. Our approach enables us to use distributed predictive and descriptive data-mining techniques to investigate datasets that are too large to be examined using centralized methods, are sensitive to privacy issues, and are under the constraint of real-time deadlines.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".