Bibliographic record
Abstract
CHILDHOOD AMBITION To be a pilot. FONDEST MEMORY Finding out I was healed of cancer and that my baby was healthy. FAVORITE MOVIE SOUNDTRACKS Easy Rider and Shrek. WILDEST DREAMS To be a writer and to go hang gliding. PROUDEST MOMENTS Independently moving from Buffalo, New York, to San Diego, California, in 1983; receiving my master's degree as a critical care clinical nurse specialist in 1992. PERFECT DAY Relaxing at our family cottage on the lake, in Long Beach, Ontario, Canada, with my husband, daughter, parents, many siblings, nieces, and nephews. FIRST HEALTH CARE JOB At age 17 as a nurse's aide at the Skilled Nursing Facility at Kenmore Mercy Hospital in Buffalo. SECRET SIN Sponge candy (a confectionary specialty of Buffalo). LAST PURCHASE A great pair of dressy, dark-brown, high-heeled sandals. INSPIRATIONS Faith; our 11-year-old daughter's battle against inflammatory bowel disease; my brother Charlie's political involvement. SCARIEST MOMENT Finding out I had microinvasive adenocarcinoma of the cervix when I was five months' pregnant. BEST VACATION Hiking and white-water rafting with my husband in Yellowstone, Grand Teton, and Glacier National Parks. FAVORITE MOVIE To Kill a Mockingbird. FAVORITE FOOD A Reuben sandwich from D. Z. Akin's, a deli in San Diego. WORST DATE'S FIRST NAME Ernie (his best friend's name was Bert). FAVORITE BOOK The Pillars of the Earth by Ken Follett. DREAM VACATION A one-month Mediterranean cruise, with stops in Spain, the south of France, Italy's coast, the Greek isles. I ASPIRE TO … Create healing environments for critically ill patients and their families. NURSING AMBITION To inspire passion for nursing in others.Figure
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.791 | 0.583 |
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".