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Record W2966768267 · doi:10.1139/facets-2018-0036

Channel geomorphology differences between stream reaches with grass- or tree-dominated riparian vegetation in southern Manitoba

2019· article· en· W2966768267 on OpenAlexaffvenueabout
Sanjayan Satchithanantham, Henry F. Wilson, Patsy Michiels, Melanie Dubois, Sheng Li, Alexander J. Koiter

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsBrandon UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRiparian zoneHydrology (agriculture)AfforestationVegetation (pathology)Environmental scienceChannel (broadcasting)FloodplainBankErosionWatershedAggradationSedimentSTREAMSPhysical geographyGeologyFluvialHabitatGeographyEcologyGeomorphologyAgroforestryStructural basin

Abstract

fetched live from OpenAlex

Removal of trees for pasture or crop production is common along the stream reaches in the Canadian Prairies, resulting in a patchwork of forested and nonforest riparian vegetation along most streams. The effect of vegetation type on channel geomorphology and potential to influence sediment dynamics was studied using eight paired reaches (forested and nonforest) within agricultural watersheds in southern Manitoba, Canada. High potential for bank erosion was observed at all sites (bank erosion hazard index scores), but Pfankuch channel stability scores were significantly higher for forested reaches compared with nonforested reaches. Furthermore, forested reaches had higher width to depth ratios, but flood-prone widths did not differ significantly, resulting in lower entrenchment ratios. Reduced channel width and cross-sectional area in nonforested reaches created an overall reduction of in-stream habitat, increased velocity, and increased potential for exceedance of channel capacity and floodplain access during high-flow events. Channel widening in response to riparian afforestation efforts has been observed in a variety of other locations globally and the results of this study suggest that widening with afforestation can still be anticipated in this region where stream gradients are low, hydrology is dominated by snowmelt, and forest cover is minimal.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

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)0.0020.001

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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes3
Has abstractyes

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