STROKE THERAPY. 1999 . Edited by Leonard P. Miller. Published by: A John Wiley & Sons Inc. 436pp C$187.50 approx.
Bibliographic record
Abstract
neuropathology and electrophysiology, to explain consciousness.Although the book can be difficult to read (if you do not have a background in philosophy or psychology), much of it is extremely well written and provides insight into phenomena we all experience.I found the discussion on consciousness and sleep particularly interesting, and now understand why certain noises (i.e. the beeping at the crosswalk which sounds just like my pager) in my neighborhood wake me up when I am on call, whereas I am not conscious of hearing them otherwise.The discussions regarding the importance of language to the development of consciousness were also thought-provoking.The book is not written for the clinical neurologist, and does not offer any guidance in the management of patients with impairments of consciousness.This is not a criticism of the book, as clinicians are not the intended audience.In summary, this is an excellent, up-to-date review of the study of consciousness.It is well laid out, and follows a logical sequence.The discussions following each chapter are particularly insightful.It is designed for those readers interested in understanding the questions regarding consciousness, but will not be helpful to the clinical neurologist for patient care.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.199 |
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".