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
MethodsA Clarius L7 (Clarius Inc., Canada) wireless ultrasound scanner was used to acquire images of the great saphenous vein (GSV).The active contour approach by Chan and Vese was used to segment and track the vessel.The user needs to specify the vein in the first image.Subsequently, the vesel is tracked automatically.The segmentation performance was assessed by comparing the results of the algorithm to the results of manual segmentation.Furthermore, the ultrasound scanner was attached to a KUKA LBR Med robotic arm (KUKA AG, Germany).The scanner was positioned on an Agar phantom containing a 10 mm duct filled with ethanol representing an artificial vessel.While moving the ultrasound scanner along the direction of the vessel, the lateral position was corrected using the segmentation results.The performance was assessed by manually segmenting the 50 images acquired during the tracking.The tracking error is defined as the remaining distance of the lesion to the images' centre line. ResultsThe segmentation error of the GSV data set was 0.47 ± 0.39 mm (mean ± std) and 0.48 ± 0.39 mm for the phantom.The mean tracking error was 0.27 ± 0.18mm and the mean segmentation time was 0.42 s. ConclusionActive contours were used to track the GSV in ultrasound images.The position of the segmented vein can be used to correct the position of an ultrasound-guided HIFU treatment unit.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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