Bibliographic record
Abstract
We are cartographers. From Gall’s early studies of bumps on the head and Broca’s documentation of a true brain–behavior relationship between language and the left hemisphere, we have been preoccupied with describing the neuroanatomic topography of language and cognitive functioning in the human brain. Technologic advances have vastly improved our precision in brain mapping. Elegantly detailed structural brain images can be obtained with high field MRI, and images of the working brain can be achieved with remarkably detailed spatial information using PET and fMRI. Limitations to this cartographic approach surfaced as we became increasingly sensitive to the temporal characteristics of neurocognitive functioning. The recent advent of electrocortical event-related potentials (ERP) has allowed us to characterize human language and cognition with remarkably high temporal resolution. However, the high temporal resolution of ERP is not accompanied by the spatial sensitivity of fMRI, and despite the high spatial resolution achieved with fMRI, this modality is quite limited in its temporal sensitivity. In this issue of Neurology , Crone et al. have achieved high spatial and temporal resolution with their electrocorticographic (ECoG) technique.1 In their study, Crone et al. monitored the regional distribution of high gamma electrocortical activity (in the 80–100 Hz range) during the …
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.040 | 0.030 |
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".