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Record W2938760149 · doi:10.1029/2018wr023419

Mobility and Bioavailability of Sediment Phosphorus in Urban Stormwater Ponds

2019· article· en· W2938760149 on OpenAlexafffund
Paul C. Frost, Clay Prater, Andrew B. Scott, Keunyea Song, Marguerite A. Xenopoulos

Bibliographic record

VenueWater Resources Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsTrent University
FundersEnvironment and Climate Change CanadaTrent University
KeywordsStormwaterEnvironmental scienceSedimentAquatic ecosystemWater columnEnvironmental chemistryPhosphorusSurface runoffCyclingBioavailabilityAlgaeHydrology (agriculture)Environmental engineeringEcologyChemistryBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Stormwater ponds can serve as retention hotspots for phosphorus (P) moving out of the urban environment. This retention may be reduced by P speciation that reduces the bioavailability of P to primary producers and alters its mobility in sediments. Here we examined the mobility and fate of dissolved P in urban stormwater ponds with a set of complementary field measurements and short‐term laboratory and field experiments. We measured the types and amount of P in water column and sediments of urban stormwater ponds. We further assessed the mobility of different P types in pond sediments in the field and rates of P release from sediment cores maintained under laboratory conditions. Finally, we assessed P uptake rates by pond algal communities using short‐term bioassay experiments. We found that dissolved organic P was highly prevalent in urban pond water and sediments and that this type of P was mobile within sediments and could be released under high or low O2 conditions. We also found highly variable P demand by algae among stormwater ponds and that algal growth responses to P was correlated to water column N:P ratios. Altogether, our results indicate an important role for organic phosphorus cycling in urban stormwater ponds, which likely constrains the overall retention efficiency in these aquatic ecosystems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

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 communication0.0000.000
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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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