Method for detecting small changes in vibrotactile perception threshold related to tactile acuity
Bibliographic record
Abstract
Two metrics, expressing the change in mechanoreceptor-specific vibrotactile thresholds at a fingertip over a time interval of months or years, and the shift in threshold from the mean values recorded from the fingertips of healthy persons, have been constructed for thresholds measured from individual fingers. The metrics assume the applicability of the acute adaptation property of mechanoreceptors, which has been confirmed by thresholds obtained from 18 forest workers on two occasions, separated by 5 years. Hence, when expressed in decibels, both threshold changes and threshold shifts may be averaged at frequencies mediated by the same receptor population to improve precision. Differences between threshold changes at frequencies mediated by the same receptor population may be used to identify inconsistent subject performance, and hence potentially erroneous results. For this group of subjects, the threshold changes and threshold shifts at frequencies believed mediated by the slowly adapting type I (SAI) (4 and 6.3 Hz) and rapidly adapting type I (FAI) (20 and 32 Hz) receptors within each finger were correlated. In these circumstances, which may be expected to occur for some work-induced and systemic peripheral neuropathies, both threshold changes and threshold shifts may be summed over SAI and FAI receptors to improve precision, and hence the potential for interpretation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".