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Impact of Seasonal Variations and Nutrient Inputs on Nitrogen Cycling and Degradation of Hexadecane by Replicated River Biofilms

2003· article· en· W2161060395 on OpenAlexaffabout
Martin R. Chénier, Danielle Beaumier, Réal Roy, Brian T. Driscoll, John R. Lawrence, Charles W. Greer

Bibliographic record

VenueApplied and Environmental Microbiology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsEnvironment and Climate Change CanadaMcGill UniversityUniversity of VictoriaBiotechnology Research Institute
Fundersnot available
KeywordsBiofilmNutrientMineralization (soil science)NitrificationDenitrificationHexadecaneNutrient cycleNitrogen cycleEnvironmental chemistryNitrogenEcologyBiologyCyclingEnvironmental scienceChemistryBacteriaSoil water

Abstract

fetched live from OpenAlex

Biofilm communities cultivated in rotating annular bioreactors using water from the South Saskatchewan River were assessed for the effects of seasonal variations and nutrient (C, N, and P) additions. Confocal laser microscopy revealed that while control biofilms were consistently dominated by bacterial biomass, the addition of nutrients shifted biofilms of summer and fall water samples to phototrophic-dominated communities. In nutrient-amended biofilms, similar patterns of nitrification, denitrification, and hexadecane mineralization rates were observed for winter and spring biofilms; fall biofilms had the highest rates of nitrification and hexadecane mineralization, and summer biofilms had the highest rates of denitrification. Very low rates of all measured activities were detected in control biofilms (without nutrient addition) regardless of season. Nutrient addition caused large increases in hexadecane mineralization and denitrification rates but only modest increases, if any, in nitrification rates, depending upon the season. Generally, both alkB and nirK were more readily PCR amplified from nutrient-amended biofilms. Both genes were amplified from all samples except for nirK from the fall control biofilm. It appears that bacterial production in the South Saskatchewan River water is limited by the availability of nutrients and that biofilm activities and composition vary with nutrient availability and time of year.

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 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 score0.050
Threshold uncertainty score0.464

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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.

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

Citations92
Published2003
Admission routes2
Has abstractyes

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