Erratum: Occurrence and Risk Factors of Dog Bites in Northern Indigenous Communities: A Scoping Review
Bibliographic record
Abstract
Due to a production error, the references were incorrectly numbered in Table 4 and in one paragraph.A correction has been made to the section Results, subsection Characteristics of Sources of Evidence, paragraph one:"The first article included was published in 2007 (35), but most studies (6/8) were published between 2010 and 2019 (Table 1). The northern Indigenous communities included or mentioned were Inuit from Nunavik, Canada (3/8) (7,11,20), Sahtu from Northwest Territories, Canada (1/8) (34), Cree and Assiniboine from Saskatchewan, Canada (2/8) (36, 37), and unspecified Natives from Alaska, USA (2/8) (10, 35). One of the United States studies (10) also compared dog bite injuries among children from non-Nordic (American Indian) and Nordic (Alaska Native) Indigenous communities and mentioned the Navajo and other American Indian communities from the USA as well. We found no publications from Eurasia."A correction has been made to The publisher apologizes for this mistake. The original version of this article has been updated. @POF Republish files were requested post-publication but we failed to update them correctly. So the files uploaded on 09 May already have some corrections implemented. What still needs correcting is the corrections sent on June 16, by the author in case 04827053. They are attached here.
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 | 0.025 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".