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
Machine Learning (ML) applications are proliferating in the enterprise. Increasingly enterprise data are used to build sophisticated ML models to assist critical business functions. Relational data which are prevalent in enterprise applications are typically normalized; as a result data have to be denormalized via primary/foreign-key joins to be provided as input to ML algorithms. In this paper we study the implementation of popular nonlinear ML models and in particular independent Gaussian Mixture Models (IGMM) over normalized data. For the case of IGMM we propose algorithms taking the statistical properties of the Gaussians into account to construct mixture models, factorizing the computation. In that way we demonstrate that we can conduct the training of the models much faster compared to other applicable approaches, without any loss in accuracy. We present the results of a thorough experimental evaluation, varying several parameters of the input relations involved and demonstrate that our proposals both for the case of IGMM yield drastic performance improvements which become increasingly higher as parameters of the underlying data vary, without any loss in accuracy.
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 | 0.002 | 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 it