Bibliographic record
Abstract
Within the publishing industry, article numbering has emerged as an easy and efficient way to cite journal articles. Elsevier has successfully rolled out article numbering to its multidisciplinary open access journal, Heliyon, along with more than 1600 other journals, and the academic community has responded positively to this initiative. Building upon the positive feedback, we are thrilled to announce the introduction of article numbering to SLAS Discovery, effective February 2024. A unique article number is an abbreviated form of an article's DOI - digital object identifier. Citing an article with an article number is very simple: the article number is used instead of the page range in the citation.[2]Van der Geer J, Hanraads JAJ, Lupton RA. The art of writing a scientific article. Heliyon. 2018; 19:100205. https://doi.org/10.1016/j.heliyon.2018.100205. Journal volumes and issue numbers will remain in place. However, SLAS Discovery will now use article numbering to identify specific articles. Introducing article numbers brings several benefits for the journal and its readers and authors. •More flexible reading: Article content can be optimized based on the device used to access it, supporting reading on-the-move, without needing to know how many traditional print pages the article takes up.•Increased options for grouping related content: In online collections and Special Issues, articles can now be placed in any order, helping readers identify papers relevant to their research interests faster.•Faster publication: With article numbers, the version of record of the article is online and citable as soon as the proof corrections are incorporated, ensuring readers have access to the latest research faster. We are delighted that SLAS Discovery's readers and authors will now enjoy these benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.366 | 0.319 |
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".