Bibliographic record
Abstract
Dear editor, The December 2014 (Can Vet J 2014;55:1123–1126) editorial is nicely reasoned with respect to its intent and its direction apropos for those who might be afflicted by the vicissitudes of life. However, as with most such treatments of the subject, it foregoes meaningful discussion of the positive aspects of suicide (or euthanasia) as a considered and appropriate end-of-life decision. Recently, a suicide note — “Goodbye & Good Luck!” — was published by Gillian Bennett (1) and explored in the Canadian media, in one instance under the headline “A suicide note that should be read by everyone.” (2). Before that, microbiologist Dr. Donald Low videotaped an impassioned plea for the right to choose when to die and “accused Canada of not having the maturity to take on one of the most emotionally charged issues in medicine.” (3). Even the president of the Canadian Medical Association, Dr. Chris Simpson, notes that there are “examples of things where we would all agree if we were in that situation we would be looking for potentially other solutions.” (3). Suicide should not be denounced across the board but viewed considerately and appropriately in its various situational guises.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.067 | 0.065 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".