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Record W4248534266 · doi:10.1002/9781119085751.ch27

Arctic and Antarctica

2017· other· en· W4248534266 on OpenAlexaboutno aff
Anders Koch, Michael G. Bruce, Karin Ladefoged

Bibliographic record

VenueInfectious Diseases · 2017
Typeother
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsArcticInfectious disease (medical specialty)GeographyThe arcticHaemophilus influenzaeStreptococcus pneumoniaePlague (disease)DiseaseIndigenousMedicineBiologyEcologyOceanographyMicrobiologyGeologyArchaeology

Abstract

fetched live from OpenAlex

The Arctic and Antarctic regions are characterized by sparse populations living in small, isolated settlements which tend to have crowded households. Although there are regional differences, living conditions and disease patterns for these populations are relatively comparable. Scientific studies on infectious diseases in Arctic regions inhabited by indigenous people are most often performed on high-incident diseases. A number of infectious diseases such as invasive disease caused by Streptococcus pneumoniae and Haemophilus influenzae, tuberculosis, chronic otitis media, hepatitis B virus, sexually transmitted infections, Helicobacter pylori, parasitic infections, and bacterial zoonoses occur at higher rates in Arctic regions than in their southern counterparts. This chapter describes infections with known high prevalence in the Arctic regions of Alaska, Canada, Greenland, and Siberia. The infectious disease patterns for persons living in and traveling to Svalbard and the Antarctic reflect those of their corresponding populations (e.g. Norway for Svalbard and countries of origin for persons traveling to the Antarctic).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.070
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0700.026

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 head0.015
GPT teacher head0.321
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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