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Record W3080342560 · doi:10.22034/iar(20).2020.671068

Epilithic algae from an urban river preferentially use ammonium over nitrate

2020· article· en· W3080342560 on OpenAlexaff
Eduardo Cejudo, William D. Taylor, Sherry L. Schiff

Bibliographic record

VenueInternational aquatic research. · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAmmoniumNitrateNitrificationEnvironmental chemistryNitrogenAmmoniaAnammoxChemistryAquatic ecosystemAlgaeEcologyBiologyDenitrificationBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Epilithon is a taxonomically diverse assemblage of aquatic organisms which grow on rocks; this biological compartment is involved in several reactions that contribute to the dynamics of dissolved organic nitrogen in water. Using ceramic tiles colonized in an urban river, this experimental study assessed the relative importance of ammonium uptake by inhibiting nitrification blocking ammonium oxidation with acetylene, as well it tested the hypothesis that epilithon preferentially assimilated ammonium over nitrate. In our experiments, ammonium uptake by epilithon accounted for 46% – 100 % of the ammonium decrease in the water column, whereas nitrate uptake accounted for 0% – 11% of the nitrate decrease. Ammonium uptake rates ranged from 197 to 519 μmol m-2 h-1, while nitrate uptake rates were from 47 to 85 μmol N-NO3- m-2 h-1. The rate of preferential assimilation (RPI) was between 1.15 and 1.26, indicating preference for ammonium over nitrate. The results of this research provide valuable information regarding the relative contribution of algal uptake relative to nitrification in epilithon from an urban river.

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 score0.003
Threshold uncertainty score0.007

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 head0.091
GPT teacher head0.334
Teacher spread0.244 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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