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
We present a new framework for capturing videos using sensor-rich mobile devices, such as smartphones, tablets, etc. Many of today's mobile devices are equipped with a variety of sensors, including accelerometers, magnetometers and gyroscopes, which are rarely used during video capture for anything more than video stabilization. We demonstrate that these sensors, together with the information that can be extracted from the recorded video via computer vision techniques, provide a rich source of data that can be leveraged to automatically edit and "clean up" the captured video. Sensor data, for example, can be used to identify undesirable video segments that are then hidden from view. We showcase an Android video recording app that captures sensor data during video recording and is capable of automatically constructing final-cuts from the recorded video. The app uses the captured sensor data plus computer vision algorithms, such as focus analysis, face detection, etc., to filter out undesirable segments and keep visually appealing portions of the captured video to create a final cut. We also show how information from various sensors and computer vision routines can be combined to create different final cuts with little or no user input.
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 | 0.000 |
| Open science | 0.000 | 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