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Record W2014575585 · doi:10.1890/12-0825.1

Beyond best management practices: pelagic biogeochemical dynamics in urban stormwater ponds

2013· article· en· W2014575585 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEcological Applications · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsTrent University
FundersOntario Ministry of Economic Development and Innovation
KeywordsStormwaterBiogeochemical cyclePlanktonEnvironmental sciencePelagic zoneDissolved organic carbonEcologyAquatic ecosystemSuspended solidsTotal suspended solidsBiogeochemistryHydrology (agriculture)Nutrient cycleNutrientSurface runoffEnvironmental engineeringBiologySewage treatmentWastewaterChemical oxygen demand

Abstract

fetched live from OpenAlex

Urban stormwater ponds are considered to be a best management practice for flood control and the protection of downstream aquatic ecosystems from excess suspended solids and other contaminants. Following this, urban ponds are assumed to operate as unreactive settling basins, whereby their overall effectiveness in water treatment is strictly controlled by physical processes. However, pelagic microbial biogeochemical dynamics could be significant contributors to nutrient and carbon cycling in these small, constructed aquatic systems. In the present study, we examined pelagic biogeochemical dynamics in 26 stormwater ponds located in southern Ontario, Canada, during late summer. Initially, we tested to see if total suspended solids (TSS) concentration, which provides a measure of catchment disturbance, landscape stability, and pond performance, could be used as an indirect predictor of plankton stocks in stormwater ponds. Structural equation modeling (SEM) using TSS as a surrogate for external loading suggested that TSS was an imperfect predictor. TSS masked plankton-nutrient relationships and appeared to reflect autochthonous production moreso than external forces. When TSS was excluded, the SEM model explained a large amount of the variation in dissolved organic matter (DOM) characteristics (55-75%) but a small amount of the variation in plankton stocks (3-38%). Plankton stocks were correlated positively with particulate nutrients and extracellular enzyme activities, suggesting rapid recycling of the fixed nutrient and carbon pool with consequential effects on DOM. DOM characteristics across the ponds were mainly of autochthonous origin. Humic matter from the watershed formed a larger part of the DOM pool only in ponds with low productivity and low dissolved organic carbon concentrations. Our results suggest that in these small, high nutrient systems internal processes might outweigh the impact of the landscape on carbon cycles. Hence, the overall benefit that constructed ponds serve to protect downstream environments must be weighed with the biogeochemical processes that take place within the water body, which could offset pond water quality gains by supporting intense microbial metabolism. Finally, TSS did not provide a useful indication of stormwater pond biogeochemistry and was biased by autochthonous production, which could lead to erroneous TSS-based management conclusions regarding pond performance.

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.

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 score0.376
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.017

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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