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Record W6910946322 · doi:10.5066/p9a6p2g1

Timing of Occurrence of Waterfowl in U.S. Counties and Canadian Counties, Boroughs, Census Districts, and Other Populated Area Designations with Modeled Exposure Status to Highly Pathogenic Avian Influenza Virus in 2021-2022

2024· dataset· en· W6910946322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsCensusGeospatial analysisWaterfowlInfluenza A virus subtype H5N1Highly pathogenicGeographic information systemShapefile

Abstract

fetched live from OpenAlex

This data provides county level occurrence information for all individuals used in modelling potential exposure and spread of highly pathogenic avian influenza (HPAIv) from the 2021-2022 North American outbreak. The data set contains individual identifiers and taxa information, an indicator of exposure, exposure status (Susceptible, Exposed by HPAIv detection in the county, or Exposed by secondary contact with an exposed bird), and date of first occurrence of each individual bird and that bird's exposure status within each visited county. Herein, county refers to any county, parish, borough, census area, or geographic region identified in the associated geospatial data US_CAN_AI.shp (ESRI shapefile format). Occurrence was determined using a spatial join procedure between GPS relocations of individuals and this geospatial dataset.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.230
Teacher spread0.207 · 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

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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