Towards developing a test of global motion for use with Paralympic athletes
Bibliographic record
Abstract
The Paralympic classification system for visual impairment only assesses static visual acuity and static visual field despite many Paralympic sports being dynamic in nature. As a first step towards determining whether motion perception tests should be used in Paralympic classification, we assessed whether motion coherence thresholds could be measured when visual acuity or visual fields were impaired at levels consistent with the current Paralympic classification criteria. Visual acuity and visual field impairments corresponding to Paralympic classification criteria were simulated in normally sighted individuals and motion coherence thresholds were measured. Mild-to-moderate visual acuity impairments had no effect on motion coherence thresholds. The most severe Paralympic class of acuity impairment (≥2.6 logMAR) significantly elevated thresholds. A trend towards superior motion coherence thresholds in the peripheral visual field compared to the central visual field was also present. Global motion perception appears to be measurable under simulated visual impairments that are consistent with the Paralympic classification. Poorer global motion perception was found for visual acuities >2.6 logMAR and visual fields <10° in diameter. Further research is needed to investigate the relationship between global motion perception and sports performance in athletes with real visual impairment.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".