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Record W3159466905 · doi:10.1016/j.foreco.2021.119259

Glyphosate remains in forest plant tissues for a decade or more

2021· article· en· W3159466905 on OpenAlexafffund
Nicole Botten, Lisa J. Wood, Jeffery R. Werner

Bibliographic record

VenueForest Ecology and Management · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsMinistry of ForestsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlyphosateAgroforestryEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Glyphosate-based herbicides are highly effective, non-selective, and broad-spectrum herbicides that have been used in British Columbia’s forest industry since the early 1980’s. Over this time, long-term persistence of glyphosate has not been measured, largely due to the inability to analyze glyphosate at low concentrations. Given the advancements in analytical techniques that are now available, we have extended the persistence curve of glyphosate to elucidate the actual length of time of persistence in northern British Columbia, rather than relying on estimations of persistence based on half-life curves that are quite often modelled from incomparable environments. We collected plant tissues from five forest understory perennial species growing in two distinct biogeoclimatic regions of northern BC to map out how glyphosate residue quantities change over time according to species, plant tissue type, and climate regime. We found that residues persisted for up to 12 years in some tissue types, and that root tissues generally retained glyphosate residues longer than shoot tissue types. We also found that samples from the colder, more northern biogeoclimatic zone investigated retained significantly higher levels of glyphosate for longer than samples collected from the warmer biogeoclimatic zone.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.830

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 teacher head, 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

Citations51
Published2021
Admission routes2
Has abstractyes

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