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Record W2921863917 · doi:10.1038/s41598-019-40369-0

A global assessment of freshwater mollusk shell oxygen isotope signatures and their relation to precipitation and stream water

2019· article· en· W2921863917 on OpenAlexaff
Laurent Pfister, Cléa Grave, Jean‐Nicolas Beisel, Jeffrey J. McDonnell

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersFonds National de la Recherche Luxembourg
KeywordsBiogeochemical cycleEnvironmental scienceIsotopeIsotopes of oxygenSnowmeltStable isotope ratioPrecipitationHydrology (agriculture)EcologySurface runoffGeologyBiologyGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract Records of δ18O in stream flow are critical for understanding and modeling hydrological, ecological, biogeochemical and atmospheric processes. However, the number of such records are extremely limited globally and the length of such time series are usually less than a decade. This situation severely handicaps their use in model testing and evaluation. Here we present a global assessment of freshwater mollusk (bivalves & gastropods) isotope data from 25 river basins that have stream water isotope values, water temperature data and shell material isotope signatures. Our data span a latitude range of 37.50°S to 52.06°N. We show that δ18O signatures in freshwater mollusks are able to explain 95% of the variance of stream water δ18O. We use shell δ18O values and water temperature data to reconstruct stream water δ18O signatures. With freshwater mussel life expectancy ranging from a few years up to 200 years, this translation of mollusk metabolic properties into long term stream water isotope records is a promising approach for substantially extending global stream water isotope records in time and space.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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 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

Citations27
Published2019
Admission routes1
Has abstractyes

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