Influence of calibration protocols afor a pressure-sensing walkway on akinetic and temporospatial aparameters
Bibliographic record
Abstract
Objectives: To evaluate the influence on the kinetic and temporospatial parameters of calibration protocols with point and step techniques for a pressure-sensing walkway. Methods: Nine Labrador dogs were used. Two protocols of point calibration technique (C1 and C2) and eight protocols of step calibration technique (C3 to C10) were performed. In C1, weight was added to a stool to match the body mass of each dog. In C2, weight was added to the stool to match a 46.1 kg person. The other eight calibration protocols represented combinations of the following factors: 46.1 kg and 96.1 kg persons, barefoot or wearing sneakers, and stepping onto the platform with one or two feet. Results: The calibration protocols did not affect the temporospatial variables or percentages of body weight (%BW) distribution. Significant differences were found in both PVI (peak vertical force) and VI (vertical impulse) between barefoot versus wearing sneakers, 46.1 kg versus 96.1 kg person, and stepping onto the platform with one foot versus two feet. When comparing C1 with other protocols, significant differences were observed in PVF and VI for both forelimbs and hindlimbs. When comparing C2 with other protocols, significant differences were observed in PVF and VI for both forelimbs and hindlimbs in all protocols. Clinical significance: The PVF and VI were influenced by the calibration protocol used, but the %BW distribution and temporospatial parameters were not. Using the same calibration protocol for all dogs within the same group eliminated the variability of the kinetic data caused by the calibration.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".