Bibliographic record
Abstract
MARTA STERN JOHNSON, RN, OCN, works at Austin Cancer Centers in TexasI'd spoken to you only that morning in October Far away/wayward daughter to fading mother Love and guilt coursing through my veins in equal parts How can I have strayed A thousand miles and twenty years away Now that your mind has atrophied Time twisting in sweet disordered spirals You still remember my voice I drive my car through a balmy October day Loving you, sadness shadowing me When I am suddenly distracted by fluttering clouds A migration of monarch butterflies Dancing lazily through the air en route to Mexico Masses of orange and black As disordered and fanciful as your mind They will only come to rest when then they settle On the bark of oyamel trees I know that half of the butterflies will die Before their springtime return to Canada You too will be gone before the spring Your dancing swirling mind will come to rest With the monarchs left behind You will remain there In the warm air and bright colors And send your love in fluttering clouds That will transcend my shadowing sadness Will I remember your voice Submissions are welcome from oncologists, oncology nurses, and other cancer caregivers. E-mail only, please, to: [email protected], and include affiliation/title, address, and phone number, along with a photo, if available.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.147 | 0.080 |
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".