Bibliographic record
Abstract
At a time when you knew, you alone, that death was soon, sharing a second joint over the draughts, our game became progressively ludicrous and the music progressively profound. You smiled suddenly and said with unexpected intensity, “All knowledge is predictive.” When you were young and I even younger you showed me the way through the wood to the hidden pool. Five yews pinned it round, then other smaller trees, and box. With its banked lips it was shielded from the wind, so that on a dry day it was to the bright sky a white reply, broken by branches, sun comprehending glass. The yews were easy to climb. One had a wayward bough hanging over the pond like a muscular arm. You showed me how to sit quietly in the groove between the muscles and wait for animals. I preferred, however, to drop stones, bigger and bigger, and watch the ripples move outward and inward until the image was calm again. Friction existed even there. Given my first watch, I recorded again and again the time between splash and second calm. Now in church, as I write in the back of my hymn book, decades fall away with the turn of a page. Would you smile at this gathering to speed you on with music? Is there any sound on the other shore for men of destiny? You will send no sign. Would you laugh at the sentimentality — tomorrow I retrace the path to the pool — of this fool who thinks you and identity aloft and laughing? J.S. Huntley Lecturer in Orthopaedics and Trauma University of Edinburgh Edinburgh, Scotland
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.644 | 0.349 |
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".