Bibliographic record
Abstract
THLA continues to provide local health librarians from a variety of workplaces with opportunities for learning, sharing, and socializing.As the new year begins, we are fortunate to be able to reflect on the events and accomplishments of 2010Á2011. EventsIt was a busy year.Last November, THLA jointly cosponsored the MLA educational webcast ABCs of e-books: Strategies for the Medical Library with the Health Science Information Consortium of Toronto.It was well attended and very informative, both providing helpful overview information and practical suggestions that were very welcome for smaller libraries embarking on e-book collections.Our holiday social in November was enjoyed by all with an abundance of prizes provided by our supporters and donors, including Ovid.In March, our members were kept informed about the progress of the Canadian Virtual Health Library through a webinar hosted by Orvie Dingwall and Jennifer Bayne.In April, the Health Sciences Library at St. Michael's Hospital arranged a tour of its library and new home, the Li Ka Shing International Healthcare Centre.It is an impressive, state of the art centre; and it was encouraging to find the library prominently and strategically positioned within the centre.Finally, the year's activities ended with the AGM on May 5th, hosted by the Hospital Library and Archives at The Hospital for Sick Children.The guest speaker was Dr.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.504 | 0.238 |
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".