MétaCan
Menu
Back to cohort
Record W4286229255 · doi:10.3389/fvets.2022.971946

Erratum: Occurrence and Risk Factors of Dog Bites in Northern Indigenous Communities: A Scoping Review

2022· review· en· W4286229255 on OpenAlexaboutno aff
Frontiers Production Office

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
Fundersnot available
KeywordsParagraphIndigenousMistakeGeographyTable (database)NavajoEthnologyLibrary scienceSocioeconomicsHistoryPolitical scienceEcologyLawSociologyBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.341
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Veterinary ScienceSame topicRabies epidemiology and controlFrench-language works237,207