Investigators' Workshop Sunday Afternoon Session I�2:15 p.m.-3:45 p.m.
Bibliographic record
Abstract
Mary Lou Smith*,† and William D. Gaillard‡*Psychology, University of Toronto, Mississauga, ON, Canada; †Neurology, Hospital for Sick Children, Toronto, ON, Canada and ‡Neurology, Children's National Medical Center, Washington, DC Summary: fMRI has been used successfully in epilepsy patients to map language networks and predict outcome. Only recently has the fMRI of memory begun to elucidate memory networks and inform practice. This IW is designed to provide the current state of fMRI memory investigations and applications in patients with localization related epilepsy. Dr. Mary Pat McAndrews will discuss work on activation in the mesial temporal regions and other components of the autobiographical memory network. She will focus on the relationship of activation strength to pre- and post-operative memory performance as well as electrophysiological data from patients with depth electrode recordings. Dr Silvia Bonelli will present data on material-specific based paradigms targeted to activate mesial temporal regions and their relationship to post operative memory outcomes. This work will also examine the issue of functional capacity vs. functional reserve in relation to hippocampal activation and surgical outcomes. Dr Madison Berl will present investigations on working memory with emphasis on the complicated relationships between working memory and language. The workshop will focus on obstacles to fMRI memory investigations, challenges of paradigm design and execution, and confounding factors of cross domain interference, and outcomes determination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.316 | 0.171 |
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".