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

2016· dataset· en· W4394496948 on OpenAlexaboutno aff
Éric Rosa, Claude Hillaire‐Marcel, Jean‐François Hélie, Alexandre Myre

Bibliographic record

VenueFigshare · 2016
Typedataset
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsStable isotope ratioComposition (language)Environmental scienceHydrology (agriculture)GeographyGeologyArtPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Linkages between δ18O–δ2H 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 δ2H–δ18O 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.016
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.011
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueFigshare→Same topicIsotope Analysis in Ecology→French-language works237,207→