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Record W4298142164 · doi:10.3389/ffgc.2022.953130

Comparison of intercept trap fluids and aerial spore collectors to survey fungal spores

2022· article· en· W4298142164 on OpenAlexafffund
Jean A. Bérubé, Jeremy D. Allison, Kate Van Rooyen, Cory Hughes, Patrick Gagné, Isabelle Ochoa, Jon Sweeney

Bibliographic record

VenueFrontiers in Forests and Global Change · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsCanadian Forest Service
FundersCanadian Forest ServiceU.S. Forest Service
KeywordsSporeBiologyTrap (plumbing)BotanyDNA sequencingDNAEnvironmental science

Abstract

fetched live from OpenAlex

Surveillance for early detection of non-native, invasive pathogens requires simple, sturdy, and easy-to-use collecting devices. In this study, we compared the fungal species detected in wet collection cups of Lindgren traps vs . those detected on slides with oiled cheesecloth as aerial spore collectors. DNA was extracted and amplified from both using the primers ITS1F and gITS7, and Illumina sequencing was used for the metabarcoding of fungi present in samples. In 90 samples, there were 1,277 fungal operational taxonomic units (OTUs). For fungal OTUs only detected by one collection method, insect traps had three times the number of fungal OTUs compared to slides, and this pattern persisted when analyses were restricted to pathogens and forest pathogens. Annually, thousands of insect traps are deployed in North America and the associated trap fluids have added value to forest disease research and monitoring.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.286
Teacher spread0.256 · 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
GenreEmpirical

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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Forests and Global ChangeSame topicPlant Pathogens and Fungal DiseasesFrench-language works237,207