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Record W2158482528 · doi:10.1093/plankt/fbi042

Seasonal changes in composition of the cyanobacterial community and the occurrence of hepatotoxic blooms in the eastern townships, Québec, Canada

2005· article· en· W2158482528 on OpenAlexaffabout
Anne Rolland, David F. Bird, Alessandra Giani

Bibliographic record

VenueJournal of Plankton Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsMicrocystinWater columnMicrocystisEutrophicationAnabaenaBiologyCyanobacteriaBiomass (ecology)Microcystis aeruginosaPlanktonNutrientAlgaeEcologyAbundance (ecology)PhytoplanktonEnvironmental chemistryChemistryBacteria

Abstract

fetched live from OpenAlex

Four eutrophic lakes in the eastern townships (Québec, Canada) were sampled on a biweekly basis between May and October 2001 to examine seasonal changes, and to study the role of taxonomic and environmental factors in cyanobacterial toxin production. Microcystin-LR (MC-LR) equivalent content was determined using a protein phosphatase inhibition assay on extracts of lyophilized plankton. Three of the lakes showed a similar pattern of maximum water column toxicity in late summer, while in the fourth, toxicity was highest in spring and then declined over the year. Variations in water toxicity level could be attributed to the abundance of two potentially toxigenic genera, Microcystis and Anabaena. A multiple regression model explained 75% of the variation in microcystin (MC) concentration, based on water column total nitrogen concentration (TN) and the biomass of these two genera. Microcystis and Anabaena genera appeared to be similarly toxic in all lakes. Increased water column stability, higher light extinction coefficient and a lack of dissolved nutrients were all associated with increased total biomass of toxigenic cyanobacterial genera.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.032
GPT teacher head0.277
Teacher spread0.245 · 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

Citations61
Published2005
Admission routes2
Has abstractyes

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