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Record W2162848247 · doi:10.1139/f2011-134

Long-term water quality changes in a multiple-stressor system: a diatom-based paleolimnological study of Lake Simcoe (Ontario, Canada)

2012· article· en· W2162848247 on OpenAlexafffundvenueabout
Jessica Hawryshyn, Kathleen M. Rühland, Roberto Quinlan, John P. Smol

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsYork UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of EnvironmentYork University
KeywordsDiatomPaleolimnologyEutrophicationEnvironmental scienceWater qualitySedimentEcologyClimate changeOceanographyHydrology (agriculture)GeologyBiologyNutrient

Abstract

fetched live from OpenAlex

Diatom-based paleolimnological techniques were used to assess long-term changes in the water quality of Lake Simcoe (Ontario, Canada) using 210 Pb-dated sediment cores from four sites across the lake. Modest lake-wide shifts in diatom community composition occurred in the late 19th and early 20th centuries, suggesting that early cultural disturbances (e.g., land clearance and canal construction) had relatively minor effects on Lake Simcoe water quality. However, starting in the 1930s, phosphorus loading to the lake increased, which was closely tracked by increases in diatom taxa indicative of eutrophic conditions. The most pronounced lake-wide shift in diatom assemblages occurred in the mid- to late 20th century, the nature and timing of which strongly suggested a response to regional climate warming. An additional and marked lake-wide shift in diatom assemblages occurred in the mid-1990s, coinciding with the invasion of dreissenid mussels in Lake Simcoe. Our results indicate that Lake Simcoe water quality has been affected by the interaction of numerous environmental stressors over the past two centuries, the complexity of which has been amplified by recent warming.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.040
GPT teacher head0.237
Teacher spread0.197 · 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.

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

Citations36
Published2012
Admission routes4
Has abstractyes

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