Bibliographic record
Abstract
Dear Sir, I sometimes encounter Canadian veterinarians who don’t quite see the value of being a CVMA member. They often ask: “What’s in it for me?” In his President’s message of January 2011 (Can Vet J 2011; 52:9–12), Dr. Doug Roberts did an excellent job outlining the tangible and, more important in my view, intangible benefits of being a CVMA member. The message is clear — In addition to making significant contributions to animal and human health and welfare, the CVMA represents, promotes, and defends the interests of the profession as a whole. Without the CVMA and its support of and by all Canadian veterinarians, the answer to the question “What’s in it for me?” will be painfully obvious when there’s nothing in it for anyone.
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 | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.051 | 0.054 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".