Usefulness of EndNote Software for Writing Scientific Manuscripts: A Comparative Study
Bibliographic record
Abstract
Referring from the text to the sources used and creating a bibliographic description (a reference) of each source used in an accurate and consistent way, are a fundamental part of scientific publications. This study was conducted to compare writing references and citation for scientific manuscript manually and by using EndNote software. Using a common referencing style formats (Vancouver and Harvard) we compared the time lapse and consumed that required for insertion of 20 references in a predesigned manuscript manually versus using EndNote software. In addition, the format of references was changed in different manners to find out the time required for making these changes. Time required for changing the order of citation, deletion and adding of one reference manually and by using EndNote software was calculated. The obtained data were managed statistically. Time spent for inserting one reference or all references in both formats Vancouver and Harvard manually and using EndNote software showed significant difference (P<0.05). Accurate reference style when using manual referencing Harvard versus Vancouver, it was 65% and 55% respectively, whereas it was 100% in both styles when using EndNote software (p<0.05). In conclusion; Citation using Endnote referencing software for writing manuscript significantly reduces time and improves the quality of the manuscript.
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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 | 0.026 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".