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Record W2748248552 · doi:10.1038/s41598-017-09049-9

Multiple sources and sinks of dissolved inorganic carbon across Swedish streams, refocusing the lens of stable C isotopes

2017· article· en· W2748248552 on OpenAlexaff
Audrey Campeau, Marcus B. Wallin, Reiner Giesler, Stefan Löfgren, Carl‐Magnus Mörth, Sherry L. Schiff, Jason J. Venkiteswaran, Kevin Bishop

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
FundersStockholms UniversitetSveriges LantbruksuniversitetUppsala UniversitetVetenskapsrådetKnut och Alice Wallenbergs StiftelseSvenska Forskningsrådet Formas
KeywordsSTREAMSDissolved organic carbonCarbon cycleEnvironmental scienceStable isotope ratioIsotopes of carbonEnvironmental chemistryChemistryEcologyBiologyEcosystemTotal organic carbonComputer science

Abstract

fetched live from OpenAlex

Abstract It is well established that stream dissolved inorganic carbon (DIC) fluxes play a central role in the global C cycle, yet the sources of stream DIC remain to a large extent unresolved. Here, we explore large-scale patterns in δ 13 C-DIC from streams across Sweden to separate and further quantify the sources and sinks of stream DIC. We found that stream DIC is governed by a variety of sources and sinks including biogenic and geogenic sources, CO 2 evasion, as well as in-stream processes. Although soil respiration was the main source of DIC across all streams, a geogenic DIC influence was identified in the northernmost region. All streams were affected by various degrees of atmospheric CO 2 evasion, but residual variance in δ 13 C-DIC also indicated a significant influence of in-stream metabolism and anaerobic processes. Due to those multiple sources and sinks, we emphasize that simply quantifying aquatic DIC fluxes will not be sufficient to characterise their role in the global C cycle.

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 score0.047
Threshold uncertainty score0.971

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 studies0.0010.001
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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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

Citations157
Published2017
Admission routes1
Has abstractyes

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