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Record W2295055031 · doi:10.1080/10256016.2015.1135138

Processes governing the stable isotope composition of water in the St. Lawrence river system, Canada

2016· article· en· W2295055031 on OpenAlexafffundabout
Éric Rosa, Claude Hillaire‐Marcel, Jean‐François Hélie, Alexandre Myre

Bibliographic record

VenueIsotopes in Environmental and Health Studies · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTributaryHydrology (agriculture)FluvialSnowmeltEstuaryStreamflowδ18ODrainage basinGeologyEnvironmental scienceStable isotope ratioMeteoric waterSeasonalityPeriod (music)Channel (broadcasting)SnowStructural basinOceanographyGroundwaterGeomorphologyGeographyEcology

Abstract

fetched live from OpenAlex

Linkages between δ(18)O-δ(2)H and hydrological processes have been investigated from isotopic time series recorded in the St. Lawrence River basin. Three stations were monitored from 1997 to 2008. They include the Ottawa River, the St. Lawrence River main channel at Montreal and the fluvial estuary. All sites depict seasonal isotopic cycles characterized by heavy isotope depletions during the snowmelt period and heavy isotope enrichments throughout the ice-free period. The data define δ(2)H-δ(18)O regression lines falling below the meteoric water line. In the Ottawa River, calculations suggest that approximately 8 % of the total inflow to the basin is lost through evaporation. In the St. Lawrence River main channel, seasonal isotopic fluctuations most likely reflect hydrological processes occurring within the Great Lakes and mixing with tributaries located downstream. In the St. Lawrence River fluvial estuary, isotopic data allow partitioning streamflow components and suggest that the recorded seasonal variations mainly respond to mixing processes.

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 score0.137
Threshold uncertainty score0.781

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 head0.016
GPT teacher head0.213
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.

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

Citations19
Published2016
Admission routes3
Has abstractyes

Explore more

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