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
Ear biometrics has known an increase of interest from the computer vision research community in recent years. This is mainly because ear geometric features can be extracted in a non-intrusive way, are unique to each individual and does not change over time. Different techniques were proposed to extract ear features in 2D and 3D space and use them in a person recognition system. In this work, we propose Deep-Ear a deep convolutional residual network to perform ear recognition. The proposed algorithm uses a 50 layers deep residual network (ResNet50) as a feature extractor followed by 2 fully connected layers and a final softmax layer for classification. Experimental tests were performed on AMI-DB ear dataset. The obtained top-1 accuracy is equal to 95.67% and a top-3 accuracy is 99.67%. These results show that the proposed architecture is promising in developing a robust feature-free ear recognition technique based on deep learning.
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 | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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