Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Outliers are data objects with different characteristics compared to other data objects. Exploring the diverse and dynamic web data for outliers is more interesting than finding outliers in numeric data sets. Interestingly, the existing web mining algorithms have concentrated on finding patterns that are frequent while discarding the less frequent ones that are likely to contain the outlying data. This paper refers to outliers present on the web as web outliers to distinguish them from traditional outliers. Web outliers are data objects that show significantly different characteristics than other web data. Although the presence of web outliers appears obvious, there is neither formal definition for web outliers nor algorithms for mining them. Secondly, traditional outlier mining algorithms designed solely for numeric data sets are inappropriate for mining web outliers. This paper establishes the presence of web outliers and discusses some practical applications of web outlier mining. Finally, we present taxonomy for web outliers and propose a general framework for mining web content out.
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.
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 | 0.000 |
| Open science | 0.001 | 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