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Record W2198528114 · doi:10.1139/cjfas-2015-0249

Interannual and long-term changes in the trophic state of a multibasin lake: effects of morphology, climate, winter aeration, and beaver activity

2015· article· en· W2198528114 on OpenAlexvenueno aff
Dale M. Robertson, William J. Rose, Paul C. Reneau

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersU.S. Geological SurveyWisconsin Department of Natural Resources
KeywordsEnvironmental scienceBeaverTrophic levelEutrophicationPrecipitationStructural basinWater qualityHydrology (agriculture)Temperate climateProductivityTrophic state indexBayEcologyOceanographyNutrientGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Little St. Germain Lake (LSG), a relatively pristine multibasin lake in Wisconsin, USA, was examined to determine how morphologic (internal), climatic (external), anthropogenic (winter aeration), and natural (beaver activity) factors affect the trophic state (phosphorus, P; chlorophyll, CHL; and Secchi depth, SD) of each of its basins. Basins intercepting the main flow and external P sources had highest P and CHL and shallowest SD. Internal loading in shallow, polymictic basins caused P and CHL to increase and SD to decrease as summer progressed. Winter aeration used to eliminate winterkill increased summer internal P loading and decreased water quality, while reductions in upstream beaver impoundments had little effect on water quality. Variations in air temperature and precipitation affected each basin differently. Warmer air temperatures increased productivity throughout the lake and decreased clarity in less eutrophic basins. Increased precipitation increased P in the basins intercepting the main flow but had little effect on the isolated deep West Bay. These relations are used to project effects of future climatic changes on LSG and other temperate lakes.

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 score0.283
Threshold uncertainty score0.728

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 studies0.0000.002
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 head0.018
GPT teacher head0.218
Teacher spread0.201 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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