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Record W2998069621 · doi:10.1002/hyp.13664

The isotope hydrology of the Muskoka River Watershed, Ontario, Canada

2019· article· en· W2998069621 on OpenAlexafffundabout
April L. James, Emily Dusome, Tim Field, Huaxia Yao, Chris McConnell, Andy D. Beaton, Arghavan Tafvizi

Bibliographic record

VenueHydrological Processes · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsLaurentian UniversityMinistry of EnvironmentMinistry of the Environment, Conservation and ParksMinistry of Natural Resources and ForestryNipissing University
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaCanada Excellence Research Chairs, Government of CanadaNipissing University
KeywordsHydrology (agriculture)SnowmeltWatershedSurface waterGroundwaterEnvironmental scienceWater qualityStreamflowGeologyDrainage basinSnowEcologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Stable isotope tracers of δ 18 O and δ 2 H are increasingly being applied in the study of water cycling in regional‐scale watersheds in which human activities, like river regulation, are important influences. In 2015, δ 18 O and δ 2 H were integrated into a water quality survey in the Muskoka River Watershed with the aim to provide new regional‐scale characterization of isotope hydrology in the 5,100‐km 2 watershed located on the Canadian Shield in central Ontario, Canada. The forest dominated region includes ~78,000 ha of lakes, 42 water control structures, and 11 generating stations, categorized as “run of river.” Within the watershed, stable isotope tracers have long been integrated into hydrologic process studies of both headwater catchments and lakes. Here, monthly surveys of δ 18 O and δ 2 H in river flow were conducted in the watershed between April 2015 and November 2016 (173 surface water samples from 10 river stations). Temporal patterns of stable isotopes in river water reflect seasonal influences of snowmelt and summer‐time evaporative fractionation. Spatial patterns, including differences observed during extreme flood levels experienced in the spring of 2016, reflect variation in source contributions to river flow (e.g., snowmelt or groundwater versus evaporatively enriched lake storage), suggesting more local influences (e.g., glacial outwash deposits). Evidence of combined influences of source mixing and evaporative fractionation could, in future, support application of tracer‐enabled hydrological modelling, estimation of mean transit times and, as such, contribute to studies of water quality and water resources in the region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.007
GPT teacher head0.180
Teacher spread0.172 · 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

Citations14
Published2019
Admission routes3
Has abstractyes

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