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Record W1942822795 · doi:10.1002/hyp.10627

Relative contributions of stream concentration, stream discharge and shoreline load to base cation trends in Red Chalk and Harp lakes, south-central Ontario, Canada

2015· article· en· W1942822795 on OpenAlexaffabout
Huaxia Yao, April L. James, Christopher McConnell, Brian Turnbull, Timothy Field, Keith M. Somers

Bibliographic record

VenueHydrological Processes · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsMinistry of the Environment, Conservation and ParksMinistry of EnvironmentNipissing University
FundersMinistry of Environment
KeywordsShoreHydrology (agriculture)Environmental scienceBase flowHARPDischargeSTREAMSGeologyDrainage basinOceanographyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

We evaluated the importance and contributions of changes in stream base cation concentration, stream discharge and lake shoreline load to changes in base cation concentrations in two small inland lakes in south-central Ontario. The shoreline load from ungauged drainage areas was calculated with a mass balance equation. An evaluation method based on the partial Mann–Kendall test quantified the relative contributions of these three explanatory drivers to the observed trends in lake concentration of Ca, K, Mg and Na. Over a 29-year period (1978–2006) at Red Chalk and Harp lakes, declines in stream concentration and discharge were correlated with the declines in lake concentrations of all base cations (except for Na in Harp Lake), with contributions of 21–81% from stream concentration and 12–58% from discharge, whereas the shoreline load had little contribution to observed trends. The observed unusual increase in Na concentration at Harp Lake was correlated with the increase in stream Na concentration and additional load from road salts, with the shoreline load contributing a substantial 37%. These results may be applicable to numerous inland lakes with similar site conditions, including lakes found on the Canadian Shield. Copyright © 2015 John Wiley & Sons, Ltd.

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 score0.557
Threshold uncertainty score0.715

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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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

Citations22
Published2015
Admission routes2
Has abstractyes

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