Bibliographic record
Abstract
The curtain draws back,An agonizing scene begins to unfold.A father is struggling to be stoic,the mother’s eyes are cold.Their child lays in the stretcher,a veteran of many a procedure.The bruises on her innocent skin,a most prominent feature.They look quite surprised,even a bit startled to see my face.“Don’t worry,” says my colleague,“This is not his first case.”“We’ve heard of your services,” says the mom,while barely holding back her tears.“We’re so glad you could stop by,” says the dad,while he struggles to confront his fears.I know my role and calling,I’ve trained for it extensively.I aim to help calm the soul,and do so inexpensively.Surgery is a very scary time,the anxiety tends to run high.“We have no choice to proceed,”the parents exclaim with a sigh.I bravely approach the child,I comfort her as she ponders her future.Her failing chest and heartwill soon meet the scalpel and the suture.I channel all of my strength, empathy, and affection,straight into that innocent child.She sheds a tear and accepts what is coming,with a long road ahead, her recovery won’t be mild.Alas it is time for me to leave,the surgery is about to start.I jump off the bed and wag my tail rigorously,I am a good boy and have hopefully done my part.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.316 | 0.176 |
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".