Bibliographic record
Abstract
Although the behavioral effects of damage to the frontal lobes date back to at least the late 19th century even midway through the 20th century very little was known about human frontal lobe function and there was a general consensus that the frontal lobe did not play a key role in cognition. This all changed when Brenda Milner published a chapter in a 1964 volume entitled: The Frontal Granular Cortex and Behavior. Milner’s chapter, “Some effects of frontal lobectomy in man,” was the first systematic study of the effect of frontal lobe excisions on cognition in human patients. Milner had access to a unique population of frontal excision patients at the Montreal Neurological Institute that were being treated by Wilder Penfield and his associates for a wide range of neurological disorders, including intractable epilepsy. Milner and her colleagues engaged in a more than 50-year study that has had a formidable impact on our understanding of frontal lobe function. Paralleling studies of frontal lobe function in non-humans they influence on understanding the evolution and function of the prefrontal cortex of mammals. Thus, although Brenda Milner is best known for her studies of human memory, she has had an equally important contribution to our understanding of the frontal lobes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".