16° Congreso Mundial de la Sociedad Internacional de Neuromodulación (INS 2024)
Bibliographic record
Abstract
Si bien en el congreso anterior de la INS en Barcelona se puso la vara muy alta, Vancouver consiguió superarla. Esto demuestra el crecimiento de esta sociedad científica y el entusiasmo y visión de sus dirigentes en un proceso de mejora continua en la promoción de esta moderna y pujante rama de la ciencia. Como resumen general de ese Congreso, podemos ratificar que Vancouver marcó un nuevo hito en la historia de los congresos mundiales de la INS por su calidad científica y especialmente por el ambiente de camaradería que no suele verse en otros ámbitos científicos, con la unión ideal de lo académico y lo social. Es por todo esto que invito a agendar el próximo congreso mundial del 9 al 14 de mayo en Lisboa, Portugal, que ya tiene su página web (https://2026.ins-congress.com/) para suscribirse al newsletter y recibir las últimas novedades del evento. Nos veremos pronto.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.135 | 0.044 |
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".