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Record W2105256497 · doi:10.82308/27032

Comparison of two constructed wetland substrates for reducing phosphorus and nitrogen pollution in agricultural runoff

2008· article· en· W2105256497 on OpenAlexaboutno aff
Charlotte R. Yates

Bibliographic record

VenueeScholarship@McGill (McGill) · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsSurface runoffEnvironmental sciencePhosphorusNutrient pollutionPollutionWetlandAgricultureWater pollutionNitrogenNonpoint source pollutionAgricultural pollutionEnvironmental engineeringWater resource managementEnvironmental protectionHydrology (agriculture)Environmental chemistryGeographyEcologyChemistryEngineering

Abstract

fetched live from OpenAlex

Phosphorus and nitrogen present in runoff from agricultural land is a primary freshwater pollution source in southern Quebec. The focus of this study was to optimize a constructed wetland for use as a best management practice and the specific aim was to determine if substrate type influences its phosphorus and nitrogen reduction capabilities. The pilot-scale constructed wetland site was located 3 km north of McGill's Macdonald campus in Ste-Anne-de-Bellevue, Quebec, Canada. Three tank replicates filled with sandy clay loam soil, and three with a sandy soil were planted with cattails (Typha latifolia L.) and reed canary grass (Phalaris arundinaceae L.). From July to September 2007, the tanks were flooded continuously with an artificial runoff wastewater, containing 10 mg N L-1 as nitrate 0.3 mg P L-1 as orthophosphate. Results show that there was no significant difference in P removal between the two soil types and both retained approximately 41%. The sandy clay loam soil outperformed the sandy soil in N removal, with 63% and 40% retained respectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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 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

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicConstructed Wetlands for Wastewater TreatmentFrench-language works237,207