Bibliographic record
Abstract
Log in or Register Subscribe to journalSubscribe Get new issue alertsGet alerts Enter your Email address: Wolters Kluwer Health may email you for journal alerts and information, but is committed to maintaining your privacy and will not share your personal information without your express consent. For more information, please refer to our Privacy Policy. Subscribe to eTOC Secondary Logo Journal Logo All Articles Images Videos Podcasts Blogs Advanced Search Toggle navigation Subscribe Register Login Articles & Issues Current IssuePrevious Issues Collections Obstetric Airway ManagementMaternal EmbolismRegional Anesthesia for Cesarean SectionGeneral Anesthesia for Cesarean SectionAnalgesia for LaborObstetric HemorrhagePre-Eclampsia/EclampsiaPharmacologyTraumaInfection and SepsisMaternal ObesityMaternl Morbidity and MortalityNeonatal Morbidity and MortalityObstetric ComplicationsAnesthetic ComplicationsNon-Obstetric Maternal DiseaseCritical CareDrug Abuse in PregnancyEthicsSystems Based Practice For Authors Information for AuthorsLanguage Editing ServicesLippincott® Preprints Journal Info About the JournalEditorial BoardAdvertisingOpen AccessSubscription ServicesReprintsRights and Permissions All Articles Images Videos Podcasts Blogs Advanced Search
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.405 | 0.159 |
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".