Bibliographic record
Abstract
1 2 Y B L O O D A N D L I G H T K A R L K I R C H W E Y Photopheresis involves the removal of blood from the body and separation of its component parts. Red blood cells are returned to the body; white blood cells are treated with a photosensitizing substance and then irradiated with light prior to their return to the body, where they will convey to other cells the memory of a programmed rather than a traumatic cell death. One use of photopheresis is in treating graft versus host disease, in which the body’s immune system turns against itself. You can do it only with a great machine fashioned of mental gray in Mississauga or Raritan, something the color of syncope. First you must separate one from the other, mindful that apparent unlikenesses are not the most profound di√erences of all. The majority you may reintroduce to the place where they usefully dwelled. But now, by ine√able degrees, leaving no consent to withhold, you must mix with those that remain a quintessence activated only by light as it was divided from darkness once, until they yield to it – or rather, their belief must succumb, that they have been under siege by an oppressor with no name serving only death’s advantage, 1 3 R and they transmit to others this revision of strife into a kind of solace, and so pass out of life teaching the stubborn body it need no more resist, inculcating in memory an unexpected trust, the reviled, contested thing now carefully handed on, and imperceptibly, without arguing, with only the sublime deception of the light that enables and justifies, as it daily walks the scarred earth in its contest against reflexes too deep to be reasoned with. In memory of Tony Anderson (1981–2015) ...
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".