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Record W2315279981 · doi:10.1061/41009(333)3

Cold Climate Issues for Bioretention: Assessing Impacts of Salt and Aggregate Application on Plant Health, Media Clogging, and Groundwater Quality

2008· article· en· W2315279981 on OpenAlexaff
Chris Denich, Andrea Bradford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBioretentionEnvironmental scienceGroundwaterEnvironmental engineeringCloggingInfiltration (HVAC)StormwaterHydrology (agriculture)Surface runoffGeotechnical engineeringEngineeringMaterials scienceEcology

Abstract

fetched live from OpenAlex

Bioretention offers the potential to sustain pre-development water balances while minimizing effects on groundwater quality and providing a landscaped aesthetic. To study cold climate issues including potential trade-offs between groundwater quality and quantity and bioretention system lifespan, a salt and aggregate mixture is being applied to 10 outdoor bioretention columns with soil, mulch and vegetation layers. A detailed methodology and preliminary findings for experimental applications of the salt/sand mixture is presented. Pre testing infiltration rates for bioretention media averaged 24.8 m/day, matching published values. Background Na+ and Cl– levels within the soil matrix averaged 736–1900 mg/L and 95–621 mg/L, respectively. Preliminary mass balance results, demonstrate a consistent response to Na+ and Cl– loading, with increased Na+ output mass, and reduced Cl– mass as compared to input in all columns. The evaluation of the effects of de-icer loading on bioretention systems is essential to the expansion of the functionality of bioretention systems to achieve stormwater management objectives in cold climates.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.340

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)0.0000.000

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 head0.042
GPT teacher head0.307
Teacher spread0.266 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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