Magnetic resonance imaging study in a normal Bengal tiger (Panthera tigris) stifle joint
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the normal appearance of the bony and soft tissue structures of the stifle joint of a Bengal tiger (Panthera tigris) by low-field magnetic resonance imaging (MRI), and the use of gross anatomical dissections performed as anatomical reference. A cadaver of a mature female was imaged by MRI using specific sequences as the Spin-echo (SE) T1-weighting and Gradient-echo (GE) STIR T2-weighting sequences in sagittal, dorsal and transverse planes, with a magnet of 0.2 Tesla. The bony and articular structures were identified and labelled on anatomical dissections, as well as on the magnetic resonance (MR) images. RESULTS: MR images showed the bone, articular cartilage, menisci and ligaments of the normal tiger stifle. SE T1-weighted sequence provided excellent resolution of the subchondral bones of the femur, tibia and patella compared with the GE STIR T2-weighted MR images. Articular cartilage and synovial fluid were visualised with high signal intensity in GE STIR T2-weighted sequence, compared with SE T1-weighted sequence where they appeared with intermediate intensity signal. Menisci and ligaments of the stifle joint were visible with low signal intensity in both sequences. The infrapatellar fat pad was hyperintense on SE T1-weighted images and showed low signal intensity on GE STIR T2-weighted images. CONCLUSIONS: MRI provided adequate information of the bony and soft tissues structures of Bengal tiger stifle joints. This information can be used as initial anatomic reference for interpretation of MR stifle images and to assist in the diagnosis of diseases of this region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".