Bibliographic record
Abstract
Although 31 years have passed since the discovery of endothelin, that pioneering report, and the subsequent flood of influential studies elucidating its molecular and clinical details, have since paved the way for thousands of publications. They showed the promise of endothelin and the vast amount of work that remains to be done to fully unleash the potential this peptide possesses, both as a key physiological regulator and as a therapeutic target. Endothelin conferences and their proceedings have served as a host for many of these breakthrough studies, and in keeping with this fine tradition, Endothelin XVI will host novel research articles presented at the Sixteenth International Conference on Endothelin (ET-16) as its proceedings. On September 22-25, 2019, ET-16 was held at Kobe Port Oasis, Kobe, Japan, where numerous important discoveries were presented to the scientific community for the first time, many of which are compiled and published in this special issue. As the Editors of this special issue that comprises in-depth reviews, insightful editorials, and numerous original research articles discussing findings from various biomedical fields, we are extremely proud to present Endothelin XVI. We sincerely hope for the continued growth of this field for the benefit of the patients and the advancement of biomedical science.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".