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Record W6891314724 · doi:10.3886/e231492v1

Influenza A Virus Data from Emperor Geese, Alaska

2025· dataset· en· W6891314724 on OpenAlexaboutno aff

Bibliographic record

VenueICPSR Data Holdings · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEmperorInfluenza A virusVirusAvian influenza virusInfluenza A virus subtype H5N1Feces

Abstract

fetched live from OpenAlex

This dataset contains avian influenza A virus (IAV) sampling information for Emperor Geese in Alaska, from 2015 to 2017. The data are in three tables: 1) collection data and IAV screening results from fecal samples at several sites in southwestern Alaska, 2) results of blocking enzyme-linked immunosorbent assay (bELISA) tests for IAV antibodies in blood serum collected from nesting female Emperor geese near the Manokinak River on the Yukon-Kuskokwim River Delta, and 3) results of influenza virus microneutralization assays to test for the specific IAV subtypes in the Manokinak samples.<br>This dataset was extracted from the USGS Science Data Catalog (SDC). A full metadata record is included.<br><br>

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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.005
Open science0.0450.060
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.026

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 head0.134
GPT teacher head0.383
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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