University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)
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
Each set of raw data is obtained at a constant nominal rotational speed and load. Four fault types are collected: inner race, outer race, ball, and cage (five of each type). A total of 20 bearings are tested, where each bearing has three states of data collection: healthy, developing fault, and faulty. As a result, 60 distinct sets of data are included. For all cases, data is collected at a sampling rate of 42,000 Hz for a total 10 seconds per set of data. Raw data is provided in the folders as comma separated values files (.csv), Excel files (.xlsx), and MATLAB files (.mat) Spectrograms are created from the raw data by using the short-time Fourier transform (STFT), while using a Hanning window to convert the accelerometer and microphone values into 2D images. For each raw data file, 400 spectrogram images are created with a signal length of 512. A total of 24,000 spectrogram images are created, 12,000 images for each of the accelerometer and microphone data files. The processed data are provided in the zip files as portable network graphics files (.png).
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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