MétaCan
Menu
Back to cohort
Record W2733087971

Bioretention Cells under Cold Climate Conditions: The Effect of Freezing and Thawing on Water Infiltration and Nutrient Removal

2017· dissertation· en· W2733087971 on OpenAlexfundaboutno aff
Xin Ran Ding

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooUniversity of TorontoToronto Rehabilitation Institute
KeywordsBioretentionStormwaterInfiltration (HVAC)Environmental scienceDrainageSurface runoffSwaleNutrientNitrateLow-impact developmentCold climateHydrology (agriculture)Environmental engineeringStormwater managementChemistryGeotechnical engineeringEngineeringGeologyEcologyMaterials scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Bioretention cells are widely used to reduce urban stormwater runoff, and improve water quality. However, their efficiency under cold climate is still poorly understood. The objective of this research is to understand the effect of freeze-thaw cycles on bioretention cell treatment and hydrological. In this study, soil column experiments were conducted with undisturbed soil cores collected from a bioretention site in Ajax, Ontario. A control column (at room temperature) and an experimental column were run. The experimental column underwent six freeze-thaw cycles consisting of 3 days at -10 Ë C followed by 2 days at 10 Ë C. Nitrate and phosphate concentrations were reduced by more than 95% in the drainage of both columns. Over the course of the experiments, the difference in drainage rates in the two columns increased slightly. The results of this research demonstrate that under cold climate conditions, bioretention cells can perform well for water infiltration and treatment.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
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 studies0.0010.000
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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.

Study designBench or experimental
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 routes2
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topicUrban Stormwater Management SolutionsFrench-language works237,207