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Record W2065803775 · doi:10.1139/f00-091

Role of chemical and physical variables in regulating microcystin-LR concentration in phytoplankton of eutrophic lakes

2000· article· en· W2065803775 on OpenAlexvenueaboutno aff
Brian G. Kotak, Angeline K.-Y. Lam, Ellie E. Prepas, Steve E. Hrudey

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEutrophicationPhytoplanktonMicrocystis aeruginosaPhosphorusMicrocystinBiomass (ecology)Environmental chemistryCyanobacteriaNitrogenToxinNutrientMicrocystisChemistryMicrocystin-LRBiologyEcologyMicrobiologyBacteria

Abstract

fetched live from OpenAlex

A survey of eutrophic to hypereutrophic hardwater lakes in central Alberta was conducted to test the hypotheses that the concentration of the cyanobacterial toxin microcystin-LR (MC-LR) in phytoplankton is regulated by environmental factors that affect both the biomass of the main producer of the toxin, Microcystis aeruginosa, and the concentration of the toxin in the cells. Of all environmental factors examined, total phosphorus was the strongest correlate of both M. aeruginosa biomass and cellular MC-LR (expressed as micrograms per gram of M. aeruginosa). Microcystis aeruginosa biomass was also strongly negatively related to the total nitrogen to total phosphorus ratio (TN:TP) and inorganic nitrogen (NO 2 - + NO 3 - , NH 4 + ). A univariate regression model of TN:TP explained the most variation in MC-LR concentration (expressed as nanograms of cellular toxin per litre) in mixed phytoplankton communities. This study indicated that MC-LR dynamics in phytoplankton of lakes was related to changes in the concentration and ratio of phosphorus and nitrogen.

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 score0.277
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations165
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicAquatic Ecosystems and Phytoplankton DynamicsFrench-language works237,207