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Record W4245074665 · doi:10.24124/2017/54740

An assessment of vegetation characteristics and hydrologic flow pathways on the effectiveness of vegetated buffer strips for phosphorus reduction in an agricultural watershed

2017· dissertation· en· W4245074665 on OpenAlexaboutno aff
Kristen Kieta

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBuffer stripSurface runoffEnvironmental scienceVegetation (pathology)Hydrology (agriculture)WatershedPhosphorusSoil waterAgricultureVegetation coverEnvironmental engineeringAgronomySoil scienceEcologyChemistryGeologyGrazingGeotechnical engineering

Abstract

fetched live from OpenAlex

Vegetated buffer strips are a management practice implemented in agricultural landscapes because of their effectiveness in reducing the transport of phosphorus (P) to surface water. However, in northern climates, buffers can become a source of P when soils are frozen and vegetation is dead or dormant during the most significant runoff period. This research investigated buffer vegetation as a potential source of P at the Morden Research Station, Manitoba. Vegetation sampling in two new buffers and an established buffer in fall 2015 and spring 2016 showed biomass P loss of 32-47% and an increase in soil Olsen P of 25-43% over winter. Thus, it is likely that much of the leached P was retained in the soil. Laboratory experiments subjected timothy grass to zero, three or six freeze-thaw cycles (FTCs), followed by extraction to leach P. Results showed an increased number of FTCs resulted in increased concentrations of leached P.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.013
GPT teacher head0.264
Teacher spread0.252 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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