Localization Analysis of Natural Toxin of Solanum tuberosum L. via Mass Spectrometric Imaging
Bibliographic record
Abstract
Motor imagery (MI), the mental rehearsal of movement, can drive the acquisition and improvement of motor skills. How motor skills can be acquired or improved without movement execution and the sensory information it affords is unclear. Moreover, the covert nature of MI necessitates an understanding of the factors that impact its effectiveness in practice and/or the ability to use MI for learning. This point regarding its use is particularly pertinent in clinical settings, where brain lesions can impede or impair ability to engage in MI-based interventions. In this symposium we discuss MI for skill acquisition in healthy participants and neurological populations. Speakers will review past evidence and present new data concerning mechanisms of skill acquisition through MI and how such mechanisms are altered after stroke. Factors that influence learning through MI, such as prior visual/motor experience and individual differences in imagery ability will be discussed. The first talk explores the development of MI based on observational and physical practice and the dependency of kinesthetic MI on a visual representation. The second talk will provide a review of some behavioural and neurophysiological methods used to assess motor system activation during MI. The third talk explores the presence of internal models and error detection/correction mechanisms in MI. In the final talk, factors which moderate the effectiveness of MI interventions after stroke will be considered. Together, this series of work will provide new insight and provide recommendations towards the understanding of MI as a modality of skill acquisition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".