Bibliographic record
Abstract
August 1981. I meet Maureen for the first time. I am struck by her energy, enthusiasm and unbridled optimism. She has recently returned to Canada from New York where she completed a fellowship in pediatric hematology. This is the beginning of a brilliant career. Over lunch in a bistro opposite McMaster University Medical Centre, she talks about bleeds and clots in infants and children. She is appalled by the lack of scientific data. Isn't it scandalous that we don't even know what are normal coagulation test results in pediatric populations? She is determined to change all that. Over the next 20 years she does. November 1986. I am in the midst of testing the effects of heparin in newborn piglets when the phone rings. The news is incomprehensible. Robbie has died. Robbie? The beautiful baby who was carried by his mom when she returned to work within days of his birth? The breezy little boy who charmed Maureen's students and staff during the traditional summer pool party with his infectious bounce and laughter? From now on, Maureen will live in a world of grief and pain. Yet, she continues on her path as ever more successful researcher and teacher with great discipline and devotion. May 1998. Maureen gives the Society for Pediatric Research Presidential Address at the Annual Meeting of the Pediatric Academic Societies in New Orleans.[ 1 ] She describes her research program. She maps out what has been accomplished, and what remains to be done. She is still passionate, but also pained. Her last slide is a picture of Robbie.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.105 | 0.048 |
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".