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
Artificial neural networks (ANNs) provide a general, effective and practical approach for learning complex target functions. However, ANNs are not suitable for handling relational data, where information about the target concept is distributed over multiple related relations. ANNs algorithms usually only explore one relation, the so-called target relation, thus excluding crucial knowledge embedded in the related so-called background relations. This paper introduces a new approach, the multiple view artificial neural networks (MVNNs) method, to address the need for bridging the gap between ANNs and relational databases. The MVNNs strategy, firstly, propagates essential information held in the target relation to all background relations. Subsequently, it exploits multiple ANNs, which explore the target concepts against the separate background relations. Thirdly, it incorporates crucial background knowledge, as obtained by the ANNs, into a meta-learning mechanism to construct the final model. Our experiments on eight data sets show that the MVNNs method achieves promising results in terms of overall accuracy obtained, when compared with two other relational data mining algorithms.
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 | 0.001 |
| 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