Investigation of white line separation under load in bovine claws with and without toe-tip necrosis
Bibliographic record
Abstract
OBJECTIVE: To compare the degree of white line separation created by increasing physiologic loads between bovine claws with and without toe-tip necrosis (TTN). SAMPLE: Cadaveric bovine hind limbs with (n = 10) and without (10) TTN. PROCEDURES: Hind limbs in which 1 or both claws had evidence of apical white line separation were considered to have TTN. Hind limbs in which neither claw had evidence of white line separation were considered controls. Each hind limb was mounted in a materials testing system with the bottom surface of the hoof angled at approximately 5° to the horizontal plane such that the apex of the claws made initial contact with the clear testing surface to simulate physiologic loading conditions. A digital camera mounted underneath the testing surface was used to obtain images of the bottom of the hoof during the application of each of 3 increasing static loads (1, 2, and 3 kN). The images were analyzed with commercial image-processing software to quantify white line separation area. RESULTS: White line separation area was significantly greater for claws with TTN than for control claws and increased as the applied load increased. White line separation was almost nonexistent in control claws and was not affected by increasing load. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that mechanical loading exacerbated TTN, but compressive loading alone, even at excessive levels, did not initiate the condition. Interventions (eg, hoof blocks) that decrease loading of affected claws may be beneficial for the treatment of TTN at its earliest stages.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".