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
Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without the help of annotations. The current CBIR systems use global features (e.g., color, texture, and shape) as image descriptors, or usefeatures extracted from segmented regions (called region-based descriptors). In the former case, descriptors are not discriminative enough at the object level and are sensitive to object occlusion or background clutter, thus fail to give satisfactory result. In the latter case, the features are sensitive to the image segmentation, which is a difficult task in its own right. In addition, the region-based descriptors are still not invariant to varying imaging conditions. In this chapter, we look at the CBIR from the object detection/recognition point of view and introduce the local feature-based image representation methods recently developed in object detection/recognition area. These local descriptors are highly distinctive and robust to imaging condition change. In addition to image representation, we also introduce the other two key issues of CBIR: similarity measurement for image descriptor comparison and the index structure for similarity search.
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 | 0.002 | 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