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Record W2902750297 · doi:10.1002/fee.1985

The overlooked impact of rising glyphosate use on phosphorus loading in agricultural watersheds

2018· review· en· W2902750297 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsMcGill UniversityGDG Environnement
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlyphosateEnvironmental sciencePesticideWatershedAgriculturePhosphorusAgronomyBiologyEcologyChemistry

Abstract

fetched live from OpenAlex

Glyphosate is the most extensively used pesticide worldwide. In addition to raising ecotoxicological concerns, the use of glyphosate adds phosphorus (P) to agricultural landscapes, influencing the accumulation and cycling of P in soil and nearby surface waters. Yet pesticides have been largely ignored when monitoring anthropogenic sources of P in agricultural watersheds. Estimating the supply of P derived from glyphosate use, both globally and in the US alone, we show that trends have markedly increased over the past two decades. Across the US , mean inputs of glyphosate‐derived P increased from 1.6 kg P km −2 in 1993 to 9.4 kg P km −2 in 2014, with values frequently exceeding 20 kg P km −2 in areas planted with glyphosate‐resistant crops. Although still a minor source of P relative to fertilizers, P inputs from glyphosate use have now reached levels comparable to those from sources for which P regulations were initiated in the past. We thus argue for greater recognition of glyphosate's influence on P flow in watershed research and management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.412
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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