Author Correction: Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study
Bibliographic record
Abstract
“This study was funded by Mitacs through the Mitacs Accelerate Program (Grant No. IT26263) and Mitacs E-Accelerate Program (Grant No. IT26655). The Mitacs Entrepreneur-Accelerate Program funds student and postdoctoral entrepreneurs to further develop the research or technology at the core of their start-up business by way of internships in collaboration with a university, professor and approved incubator. In this study, the total study funding was $15,000 CAD and was provided through Mitacs to the university (University of Toronto) for the professor (Author ED) to administer for the student/intern/entrepreneur (Author AJK) and the study expenses related to the company’s (SBI) technology under the supervision of the incubator (Health Innovation Hub). Mitacs provided 50% of the study funding. Mitacs’ contribution to the grant was matched by SBI, which provided the remaining 50%. Mitacs had no role in the study design; collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication data. However, SBI with the oversight of the University of Toronto had a role in all these aspects via Author’s AJK and ED. This publication was supported by the Canadian Institutes of Health Research (CIHR), Funding Reference Number 192124.”
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 | 0.009 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".