Open letter to journal editors on: International Consensus Radiochemistry Nomenclature Guidelines
Bibliographic record
Abstract
After observing an increased incidence of imprecise and sometimes erroneous use of nuclear chemistry, radiochemistry, and radiopharmacy related terms and nomenclature in scientific reports, an international working group of experts was assembled to address the issue.Upon extensive consultation with peers within the field of radiochemistry and radiopharmaceutical sciences over a 3-year period and an open discussion, consensus was achieved during the International Symposium on Radiopharmaceutical Sciences earlier last year in an open forum.The resulting, harmonised nomenclature recommendations have now been published with following aims given therein 1 :• Provide a reference source for nomenclature good practice in the radiopharmaceutical sciences.• Clarify the use of terms and rules concerning exclusively radiopharmaceutical terminology, i.e. nuclear-and radiochemical terms, symbols, and expressions.• Address gaps and inconsistencies in existing radiochemistry nomenclature rules.• Provide source literature for further harmonisation beyond our immediate peer group (publishers, editors, IUPAC, pharmacopoeias, etc.).To disseminate further the consensus recommendations, a summary (see below) has been prepared for ease of reference and for open dissemination across the field.We hope that your journal will consider incorporating these guidelines as part of your journals' manuscript preparation instructions.
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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 | 0.007 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.051 | 0.031 |
| Insufficient payload (model declined to judge) | 0.021 | 0.025 |
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".