The troubling story of blood-driven dementias
Bibliographic record
Abstract
In the mid 1800s, Paul Bert developed a novel model that involved the surgical fusion of two living organisms, which he named parabiosis; a term derived from the English word para (beside) and the Greek word biosis (model of life). Bert was interested in reperfusion of tissue after transplantation. He postulated that blood networks were adaptable and that if two living organisms were joined by the flank, their circulatory systems would correspondingly become coupled. This hypothesis was confirmed when he injected a tracer into the blood of one rat and detected it in the non-injected, but surgically attached animal [ 1 ]. The ability of parabiosis to definitively demonstrate the involvement, or lack of involvement, of circulating factors made it a popular technique used to answer a diverse array of research questions ranging from factors influencing obesity, to the cause of dental cavities [ 2 ].
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".