Bibliographic record
Abstract
many fans were "looking for blood", the coach's actions calmed us down and reinstated our faith in the team.Similarly, in pharmacist practice, we must all take responsibility for our actions.This attitude helps to ensure that we maintain the respect of our patients and other health care professionals.Little Plays Make a Difference: Whether it is conceding a single point in a football game or suggesting a statin for secondary prevention, little plays can add up significantly over time.We need to make the most of every situation.Attention to Detail: Whether counting players on the field or noticing trends in potassium levels, paying attention to detail is vitally important. Pride, Passion, and Inspiration:The Rider Nation can make a difference to players' morale with hats made of real watermelons, green Rider jerseys, a 6-foot gopher, flags, and face paint.In pharmacy, with all of us supporting and advocating for our profession by showing our "professional colours", we can make a difference to our profession and our patients!As I anticipate what lies ahead for our profession, I am overcome with excitement.Perhaps the dawn of a "Pharmacist Nation" is on the horizon.I wonder . . .what food should we wear on our heads?
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".