Bibliographic record
Abstract
Of the many investigators who went in search of the blood sugar-lowering hormone, three came very close to the goal. They owned patents for their extracts and, following the discoveries in Toronto, they attempted to claim their precedence. Georg Ludwig Zülzer in Berlin treated animals and patients with his acomatol – initially the results were inconsistent but in later years, together with Dr. Camille Reuter from Roche, a very effective insulin preparation was produced – sadly just at the time when the First World War began in 1914. The project ended with the war. In Chicago, Ernest Lyman Scott produced an effective extract when working on his thesis – but sadly the publication of his results was written so badly by his head of department that the manuscript passed unnoticed. Nicolai Paulescu produced insulin in Bucharest and observed positive effects in animals and patients. The results were published shortly before the work of Banting and Best, and for many years he and his Romanian colleagues fought for the recognition of his contribution to the discovery of insulin. His unforgivable extreme right-wing political activities only became known internationally after many years. The stories of the various reasons for the failures to produce a suitable extract for the treatment of diabetes is a lesson that teaches us how to avoid pitfalls in research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".