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Record W2491350455 · doi:10.1080/02705060.2016.1205526

Do human activities affect phytoplankton biomass and composition in embayments on Lake Diefenbaker?

2016· article· en· W2491350455 on OpenAlexafffundabout
Oghenemise Abirhire, Rebecca L. North, Kristine Hunter, David M. Vandergucht, Jeff J. Hudson

Bibliographic record

VenueJournal of Freshwater Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsGlobal Institute for Water SecurityWater Security AgencyUniversity of Saskatchewan
FundersWater Security AgencyNatural Sciences and Engineering Research Council of Canada
KeywordsPhytoplanktonBiomass (ecology)Water qualityAlgaeEnvironmental scienceEcologyCyanobacteriaNutrientBiology

Abstract

fetched live from OpenAlex

Lake Diefenbaker (LD) is an important source of water for southern Saskatchewan. LD is characterized by numerous embayments containing anthropogenic activities (e.g., housing, marinas, cattle watering). Many of these activities are increasing on this important reservoir in association with the rapidly developing economy of Saskatchewan. These activities may reduce water quality directly or indirectly by encouraging the growth of nuisance algae (i.e. cyanobacteria). Here, we examined phytoplankton biomass and composition in eight embayments exposed to anthropogenic activities, four unexposed embayments with no perceived human activities and six main channel sites adjacent to the embayments from June to October (2011 and 2012). Phytoplankton biomass and composition was not significantly different in exposed, unexposed embayments and main channel sites (p > 0.05), with the diatoms and cryptomonads constituting 87%–91% of the total phytoplankton biomass in both years. High flows from the South Saskatchewan River (SSR) in both years may have resulted in the rapid flushing of the embayments and dampened any localized impacts that could have resulted from anthropogenic activities as found in other studies. Hence, future study on LD should be conducted during years with low flow from the SSR when the rate of flushing of embayments will be reduced.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.885

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)0.0010.000

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations15
Published2016
Admission routes3
Has abstractyes

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