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
The present paper approaches high-frequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the feature-weighted Euclidean distance to the centroid of a training cluster. A set of micro technical indicators, traditionally employed by professional scalpers, is used in this setting. We describe procedures for removal of outliers, normalization of feature points, computation of weights of features, and classification of test points. The complexity of computation at each quote received is proportional to the number of features. In addition, processing of indicators is parallelizable and, therefore, suitable in high-frequency domains. Experiments are presented for different prediction time intervals and confidence thresholds. Predictions made 10 to 2000 milliseconds before a price change resulted in an accuracy that ranged monotonically from 97% to 75%. Finally, we observed an empirical relation between Euclidean distance in the feature space and prediction 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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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