Bibliographic record
Abstract
Dear Readers of the Journal of Neurochemistry, The Journal of Neurochemistry is the official journal of the International Society for Neurochemistry (ISN). ISN and our entire Neurochemistry community are suffering from a terrible loss as our President and good friend Professor Kazuhiro Ikenaka has finally lost his fight against cancer, passing away on October 27, 2018. ISN has not only lost a visionary President, a longtime dedicated ISN member and a brilliant neurochemist. We have all lost a kind and insightful friend who has served as a mentor to many and as a much needed and selfless bridge in many times of need. As a colleague and friend, we especially remember and will miss his humor, his thoughtful ideas, and his guidance. We will miss Kaz and his advice sadly. We have begun planning a commemoration to remember and honor Kaz at the next biennial meeting of ISN being held at Montreal in August 2019. We will celebrate his mentorship, his scientific achievements, and all the exceptional work that he has done for ISN and the entire worldwide neurochemistry community. For ISN, the ISN Council and the ISN membership, we have expressed our great sorrow, our deepest feelings and our sympathy to his wife, Yumiko, and his family. We will sorely miss Kaz as friend, mentor, and leader. Monica Carson, Interim ISN President Ralf Dringen, ISN Secretary Flávia Gomes, ISN Treasurer
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".