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Record W2078142483 · doi:10.1139/f06-180

Periphyton biomass response to changing phosphorus concentrations in a nutrient-impacted river: a new methodology for phosphorus target setting

2007· article· en· W2078142483 on OpenAlexvenueno aff
Michael J. Bowes, J.T. Smith, John Hilton, Michael M. Sturt, Patrick D. Armitage

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPeriphytonNutrientPhosphorusBiomass (ecology)Environmental scienceRiver ecosystemEnvironmental chemistryAnimal scienceEutrophicationAlgaeEcologyChemistryHydrology (agriculture)EcosystemBiologyGeology

Abstract

fetched live from OpenAlex

Nutrient modification experiments were conducted in streamside flumes to determine the concentration at which P limits algal growth in the mesotrophic River Frome, Dorset, UK. The soluble reactive P (SRP) concentration in each flume was either increased (by P addition), decreased (by precipitating P with iron(II) sulphate solution), or left unaltered (control), producing SRP concentrations ranging from 32 to 420 µ·L –1 . Increasing the ambient SRP concentration did not increase epilithic algal growth, showing that the River Frome was not P limited at 109 µ SRP·L –1 . In the P-stripped flumes, algal biomass declined as the SRP concentration fell below ~90 µ·L –1 , with a 60% biomass reduction at <40 µ SRP·L –1 . Phosphorus-diffusing periphytometers deployed in the P-stripped flumes confirmed that reduced rates of algal growth were due to P limitation rather than a physical effect of FeSO 4 addition. The ~90 µ·L –1 maximum P-limiting concentration is likely to be similar for comparable nutrient-impacted rivers. This iron-stripping approach expands the existing river nutrient-enrichment methodology so that it can be used in nutrient-impacted rivers and should allow catchment managers to produce knowledge-based P reduction targets prior to introducing remediation.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0000.000
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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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 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

Citations50
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicSoil and Water Nutrient DynamicsFrench-language works237,207