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.
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 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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".