The Principles of Biomedical Scientific Writing: Citation
Bibliographic record
Abstract
Citation, the act of properly referring to others' ideas, thoughts, or concepts, is a common and critical practice in scientific writing. Citations are used to give credit to own work, to support an argument, to acknowledge others' work, to distinguish other authors' ideas from one's work, and to direct readers to sources of information. A good citation adds to the scientific prestige of the paper and makes it more valuable to the reader. The citation has three basic elements: quoting from others, an in-text reference to the source, and bibliographic details of the source. Beyond technical skills, the citation needs an in-depth knowledge of the field and should follow basic rules, including the selection of relevant and valid sources, stating information/facts from others' work, and referring to others' work accurately and ethically. Several systems and styles are used to cite scientific sources; however, the most commonly used systems in medical sciences are 'author-date' systems (e.g., Harvard system) and numerical systems (e.g., Vancouver system). Here, we discuss how to make an accurate, complete, and ethical citation, and provide simple and practical guides to organize references in a scientific medical paper.
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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 | 0.091 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.016 |
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".