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
Spatial frequencies critical for recognition of faces are scale-dependent. Progressively coarser features of the face are utilized at smaller sizes, despite the availability of finer features. Blur removes fine details in an image, disrupting the finer features utilized for recognition at large sizes. At smaller sizes, observers utilize coarser features, and thus, recognition may be less impacted by blur. This coupling between size and critical spatial frequencies allows us to predict a regime in which observers tolerate blur better with decreasing image sizes within a range of moderate face sizes. We tested recognition of famous faces in four conditions: large-intact, small-intact, large-blurry, and small-blurry. Observers showed high recognition performance in both intact conditions. Blur significantly disrupted recognition, yet accuracy was significantly and consistently higher in the small-blurry compared to the large-blurry condition. These results are suggestive of an inherent scale-dependent mechanism that, at certain sizes, negatively impacts recognition of blurry images.
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 | 0.001 |
| 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) | 0.001 | 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