Connecting D.O.T.S.: An Education on an Organ Donation and Transplantation Program for Schools.
Bibliographic record
Abstract
Education of the public on organ donation and transplantation may be the best way to increase awareness of this lifesaving practice. Research has shown that children are open to learn about these topics. After a long journey that started with the pioneer work of Dr. Felix Cantarovich (Argentina), the positive impact of Dr. William Wall in Canadian schools, followed by the "First Global Forum on Organ Donation and Transplantation for Schools" (The Transplantation Society, 2012), the joint 2017 working group of the Canadian Society of Transplantation and The Transplantation Society, and the work of Professor Dr. Marion Siebelink (Netherlands), we were able to arrive to "Connecting D.O.T.S." (Donation and Organ Transplantation for Schools). The Connecting D.O.T.S. platform is an online, free, and easy to access platform from The Transplantation Society dedicated to educating schoolchildren around the globe on donation and organ transplantation, with modules for students, teachers, and parents.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".