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
<p>Conventional clustering approaches require a preprocessing step that estimates the correct number of cluster prior to the cluster center allocation step. In these approaches, the preprocessing step minimizes one objective function while the second step concentrates on optimization of another objective function. Inspired by MACE-means, we use a single objective function to simultaneously estimate the Correct Number of Cluster (CNC) and acquire the cluster centers. Similarly, we use the Average Central Error (ACE) as ourcost function. The proposed method, denoted by k-minimum ACE (k-MACE), improves MACE-means by rigorous calculation of probabilistic estimate of ACE. While MACE-means (Minimum ACE) only concentrates on Independent Indentically Distributed (IID) clusters, k MACE is a solution for Gaussian clusters with any covariance structure. Simulation results show superiority of k MACE over MACE means and over conven- tional clustering methods such as G-means, DBSCAN, and validity indices methods such as Calinkski Harabaz, Silhoutte, and gap index. Performance is evaluated in terms of</p>
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| 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