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Record W2801322743 · doi:10.1016/j.ejrh.2018.04.006

Isotopic and geochemical surveys of lakes in coastal B.C.: Insights into regional water balance and water quality controls

2018· article· en· W2801322743 on OpenAlexafffundabout
J. J. Gibson, S. J. Birks, Yuanbi Yi, P. D. Shaw, Michael C. Moncur

Bibliographic record

VenueJournal of Hydrology Regional Studies · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Victoria
FundersInnotech AlbertaEnvironment and Climate Change Canada
KeywordsWater qualityHydrology (agriculture)WatershedWater balanceDrainage basinGeologyElevation (ballistics)LithologyEnvironmental sciencePhysical geographyGeochemistryGeographyEcology

Abstract

fetched live from OpenAlex

This study was conducted within a 100,000 km2 area of British Columbia, (B.C.) Canada including Vancouver Island, the Georgia Basin, and the Pacific and Kitimat mountain ranges rising from the Pacific Ocean. A stable isotope mass balance method is applied to estimate evaporation loss and water yield from a remote network of 560 lakes on Vancouver Island and coastal B.C., based on helicopter sampling surveys conducted between 2008 and 2015. Spatial patterns in derived hydrological parameters are compared to water quality indicators and watershed characteristics to provide insight into water quantity and water quality relationships in the region, to be incorporated within a future critical loads assessment. Regional trends in lake water balance, underlying physical drivers, and geochemical processes potentially influencing critical loads of acidity are described. Dominant non-anthropogenic regional drivers of geochemistry include sea spray, lithology, weathering and elevation. Significant contrast is noted in alkalinity between the sedimentary and volcanic substrates on Vancouver Island and igneous intrusive substrates of the Pacific and Kitimat ranges. A positive correlation is found between elevation and water yield to lakes, while the opposite is observed for rivers, which is interpreted to reflect disconnection of low elevation lakes from regional drainage networks. This may invalidate use of river gauge data for critical loads assessment in this or similar regions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.415

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)0.0000.000

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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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

Citations15
Published2018
Admission routes3
Has abstractyes

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