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Record W3135756775 · doi:10.11575/prism/38639

Alberta Childhood COVID-19 Cohort (AB3C) Aim 3: Longitudinal Sero-Epidemiology Study First Interim Report January 31, 2021

2021· dataset· en· W3135756775 on OpenAlexfundaboutno aff
James D. Kellner

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
FundersAlberta Children's Hospital Research InstituteGenome AlbertaGovernment of Alberta
KeywordsInterimEpidemiologyCoronavirus disease 2019 (COVID-19)CohortMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Cohort study2019-20 coronavirus outbreakPediatricsVirologyGeographyOutbreakInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The AB3C study has three primary aims: 1. Establish prospective cohort study of all children in Alberta tested for or diagnosed with confirmed or probable COVID-19 infection. (Funded by Alberta Children’s Hospital Research Institute (ACHRI)) 2. Conduct a detailed multiomic precision-medicine evaluation of some children in Alberta with confirmed or probable COVID-19 infection, as well as some healthy controls. (Funded by Genome Alberta) 3. Evaluation of the adaptive immune response to SARS-CoV-2 virus in children will be conducted measuring the antibody response against COVID-19 in children with and without clinically apparent confirmed or probable COVID-19 infection in a longitudinal sero-epidemiology study over two years. (Funded by Alberta Health and ACHRI) The AB3C study has received research ethics and operational approval from the University of Calgary’s and Alberta Health Services’ Conjoint Health Research Ethics Board (CHREB). This report describes the results of the enrolment and first of four visits for Aim 3.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.160
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.007

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 head0.185
GPT teacher head0.484
Teacher spread0.299 · 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 designObservational
Domainnot available
GenreDataset

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

Citations1
Published2021
Admission routes2
Has abstractyes

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