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Record W6962949079 · doi:10.17632/pn9xdpvdvj

Data from: Wetland buffers are no substitute for landscape-scale conservation.

2019· dataset· en· W6962949079 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandAgricultureContext (archaeology)PesticideBuffer zoneContaminationNutrientAgricultural land

Abstract

fetched live from OpenAlex

Abstract of associated article: Wetlands in farmland are at risk of contamination by fertilizers and pesticides. One recommendation for reducing wetland contamination is to maintain a buffer of contiguous uncropped land around the wetland (a 'wetland buffer'). Many agricultural water protection policies around the world recommend 5–50-m wide uncropped buffers around water bodies, but it is unclear how large wetland buffers must be to effectively protect against these chemicals. In addition, it is unclear whether wetland buffers have similar—or stronger—effects on fertilizer and pesticide contamination than reducing the amount of cropped land within the larger landscape context of wetlands. Our study, conducted across 37 wetlands in eastern Ontario, Canada, addressed the following questions: (1) Does increasing buffer width, or increasing the amount of contiguous uncropped land within recommended buffer width guidelines, reduce nutrient and pesticide levels in agricultural wetlands; (2) Does increasing uncropped land cover in the broader landscape reduce nutrient and pesticide levels in agricultural wetlands; and (3) What is the relative importance of buffer size and landscape-scale uncropped cover for reducing nutrient and pesticide levels in agricultural wetlands? A rigorous site selection process was employed to minimize the correlation between buffer size and landscape-scale uncropped cover, minimize spatial gradients in these predictor variables, and minimize variation in potentially confounding variables. We obtained nutrient and pesticide data by collecting water samples from each wetland under similar weather conditions in June–July 2015. Nitrate concentrations were measured using ion chromatography, and atrazine and neonicotinoid (pesticide) concentrations using a combination of high-performance liquid chromatography and mass spectrometry. We found that nitrate, atrazine, and neonicotinoid concentrations in study wetlands were unaffected by wetland buffer size. However, concentrations of each chemical decreased with uncropped land cover in the surrounding 150 to 300-m radius landscapes. To effectively protect water in agricultural wetlands from contamination by nitrate-based fertilizers and atrazine or neonicotinoid pesticides, we recommend either increasing the policy-recommended width of wetland buffers to at least 150 m, or abandoning the buffer paradigm in favor of landscape-scale conservation. Key words: agricultural wetland; atrazine; buffer zone; land use; neonicotinoids; nitrogen; pesticides; riparian buffer; scale of effect; spatial extent; vegetated filter strip; water quality. Note: To preserve privacy of landowners, latitude and longitude data have been omitted.

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)0.001
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.386
Threshold uncertainty score0.834

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2490.058

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 head0.175
GPT teacher head0.343
Teacher spread0.168 · 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
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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