MétaCan
Menu
Back to cohort

Principles of the Atmospheric Pathway for Invasive Species Applied to Soybean Rust

2005· article· en· W2175253237 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.

Bibliographic record

VenueBioScience · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsUniversité de Montréal
FundersAnimal and Plant Health Inspection ServiceNorth Carolina State UniversityU.S. Department of Agriculture
KeywordsRust (programming language)Environmental scienceBiologyComputer science

Abstract

fetched live from OpenAlex

Aerial transport alone is seldom responsible for the introduction of nonindigenous species into distant regions; however, the capacity to use the atmospheric pathway for rapid spread in large part determines the invasive potential of organisms once they are introduced. Because physical and biological features of Earth's surface influence the routes and timing of organisms that use the atmospheric pathway, long-distance movement of aerobiota is largely regular and thus predictable. Soybean rust (Phakopsora pachyrhizi), potentially the most destructive foliar disease of soybean, recently invaded North America. The concepts presented in this article form the basis of the soybean rust aerobiology prediction system (SRAPS) that was developed to assess potential pathogen movement from South America to the United States. Output from SRAPS guided the scouting operations after the initial discovery of soybean rust in Louisiana. Subsequent observations of P. pachyrhizi in the southeastern United States provide validation of the modeling effort.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.225
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