RESEARCH Hand contour detection in
Bibliographic record
Abstract
ed ut ca ne consequences and ensure that as much function as pos-sible is regained, an intensive rehabilitation process is approach. Various clinical assessments exist to measure hand function, for example the Graded and Redefined J N E R JOURNAL OF NEUROENGINEERINGAND REHABILITATIONZariffa and Popovic Journal of NeuroEngineering and Rehabilitation 2013, 10:114http://www.jneuroengrehab.com/content/10/1/114thermore, performance in these settings is not necessarily2Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canadaundertaken following injury. Despite current best practices in rehabilitation, however, the recovery of hand function remains the top priority of individuals with tetraplegia [1]. The search for new interventions to enhance functional recovery after neurological injury is thus ongoing, and includes pharmacological interventions [2], physical and
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".