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Marked recent increases of colonial scaled chrysophytes in boreal lakes: implications for the management of taste and odour events

2004· article· en· W1998872440 on OpenAlexaffabout
Andrew M. Paterson, Brian F. Cumming, John P. Smol, Roland I. Hall

Bibliographic record

VenueFreshwater Biology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of WaterlooQueen's UniversityMinistry of the Environment, Conservation and Parks
FundersMinistry of Environment
KeywordsEcologyBorealAlgaeAbundance (ecology)EutrophicationTemporal scalesColonialismEnvironmental scienceGeographyOceanographyPhysical geographyBiologyNutrientArchaeologyGeology

Abstract

fetched live from OpenAlex

Summary 1. Lake managers suspect that taste and odour‐causing algal blooms are increasing in frequency and intensity, although long‐term monitoring records are scarce, and a number of critical scientific and management questions remain unanswered. 2. In nutrient‐poor lakes and reservoirs, these events are caused primarily by sporadic outbreaks of some chrysophyte algae, which leave identifiable markers in lake sediments. We examine the siliceous remains of these organisms in more than fifty boreal lakes at broad temporal and spatial scales. 3. Colonial scaled chrysophytes, including the taste and odour‐causing Synura petersenii , have increased markedly in more that 90% of the lakes examined since pre‐industrial times. 4. Detailed stratigraphic analyses of two lakes show a rise in the abundance of colonial taxa in the 1930s to 1950s, with a sharp increase over the past two decades. 5. An examination of biogenic silica and biological ratios in Crosson Lake, Ontario, Canada, indicate that these changes represent true increases in the absolute abundance of colonial chrysophytes. 6. Rapid increases over the past two decades indicate that these trends are the result of one or more anthropogenic stressors that are operating at a broad, regional scale.

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 score0.010
Threshold uncertainty score0.611

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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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

Citations55
Published2004
Admission routes2
Has abstractyes

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