Learning Confounds Algometric Assessment of Mechanical Thresholds in Normal Dogs
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
OBJECTIVE: To perform algometric readings in normal dogs in a design that would assess possible confounding factors. STUDY DESIGN: Prospective study. ANIMALS: Skeletally mature spayed female, intact male and castrated male retriever or retriever mix dogs without orthopedic or neurologic disease (n = 19). METHODS: Twelve common surgical sites were selected for algometric pressure testing. Threshold response was defined as a conscious recognition of the stimulus, and recorded in Newtons. Sites were tested in the same order, and the testing sequence repeated 3 times on each side of the dog. Dogs were tested in the morning and evening of the same day and was repeated 10-14 days later, allowing 4 separate data collections for each dog. RESULTS: Data were analyzed using ANOVA or ANCOVA. When all the data were included in the analysis, dog (P < .0001), order (P < .0001), site (P < .0001), site order (P = .0217), time (P < .0001), day (P < .0001) and repetition (P < .0001) all significantly affected the algometer readings. When only the first reading for each site was included in the analysis, dog (P < .0001), site (P < .0001) and sex (P < .0001) all significantly affected algometer readings. CONCLUSION: These results suggest that learning occurred over repeated collection time points, with dogs anticipating the stimulus and reacting at lower thresholds.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it