Bibliographic record
Abstract
Abstract Self-orientation perception refers to our perceived self-orientation relative to gravity. An internal representation of self-orientation is derived from sensory cues indicating either directly or indirectly the direction of gravity relative to the body. The internal representation can be measured in the laboratory or clinic using visual or haptic measures, or even the body itself. However, each measure is affected differently by the availability of cues and the observer’s actual orientation relative to gravity, suggesting multiple, simultaneous representations of gravity. Visual, vestibular, somatosensory and proprioceptive cues are combined by multisensory integration to provide the most reliable estimates. Multisensory integration provides a robust perception of self-orientation for adults but means that children have a much lower precision in judging vertical before multisensory integration mechanisms are mature. The neurophysiological basis of the perception of self-orientation is a network of brain areas reflecting the multisensory processes that underlie it. This network provides some redundancy that can be exploited for potential patient recovery. Future work will perfect models for predicting perceived self-orientation in ever more challenging situations and how we can improve performance of pilots, divers and astronauts as they explore new situations and new gravity fields, and improve how we, and especially older people, can continue to enjoy our lifelong dance with gravity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".