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Record W3085573272 · doi:10.1029/2019wr026381

Cutting Edge: A Comparison of Contemporary Practices of Riparian Buffer Retention Around Small Streams in Canada, Finland, and Sweden

2020· article· en· W3085573272 on OpenAlexaffabout
Lenka Kuglerová, Jussi Jyväsjärvi, Claire M. Ruffing, Timo Muotka, Anna Jönsson, Elisabet Andersson, John S. Richardson

Bibliographic record

VenueWater Resources Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
FundersAcademy of FinlandSvenska Forskningsrådet Formas
KeywordsSTREAMSRiparian zoneRiparian bufferBuffer zoneRiparian forestHydrology (agriculture)Environmental scienceForestryGeographyEcologyArchaeologyGeologyHabitatComputer science

Abstract

fetched live from OpenAlex

Abstract Forested riparian buffers are recommended to mitigate negative effects of forest harvesting on recipient freshwater ecosystems. Most of the current best practices of riparian buffer retention aim at larger streams. Riparian protection along small streams is thought to be lacking; however, it is not well documented. We surveyed 286 small streams flowing through recent clearcuts in three timber‐producing jurisdictions—British Columbia, Canada (BC), Finland, and Sweden. The three jurisdictions differed in riparian buffer implementation. In BC, forested buffers are not required on the smallest streams, and 45% of the sites in BC had no buffer. The average (±SE) width of voluntarily retained buffers was 15.9 m (±2.1) on each side of the stream. An operation‐free zone is mandatory around the smallest streams in BC, and 90% of the sites fulfilled these criteria. Finland and Sweden had buffers allocated to most of the surveyed streams, with average buffer width of 15.3 m (±1.4) in Finland and 4 m (±0.4) in Sweden. Most of the streams in the two Nordic countries had additional forestry‐associated impairments such as machine tracks, or soil preparation within the riparian zone. Riparian buffer width somewhat increased with stream size and slope of the riparian area, however, not in all investigated regions. We concluded that the majority of the streams surveyed in this study are insufficiently protected. We suggest that a monitoring of forestry practices and revising present forestry guidelines is needed in order to increase the protection of our smallest water courses.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.124
GPT teacher head0.328
Teacher spread0.205 · 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

Citations95
Published2020
Admission routes2
Has abstractyes

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