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Record W2981248436 · doi:10.1038/s41598-019-50552-y

Controls on Dissolved Organic Carbon Bioreactivity in River Systems

2019· article· en· W2981248436 on OpenAlexaff
Ana Soares, Jean‐François Lapierre, Balathandayuthabani Panneer Selvam, Göran Lindström, Martin Berggren

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversité de Montréal
FundersSvenska Forskningsrådet Formas
KeywordsDissolved organic carbonEnvironmental chemistryEnvironmental scienceTotal organic carbonAquatic ecosystemNutrientCarbon cycleChemistryEcologyEcosystemBiology

Abstract

fetched live from OpenAlex

. Despite its global importance, relatively little is known about environmental controls on bioreactivity of DOC as it moves through river systems with varying water residence time (WRT). Here we determined the influence of WRT and landscape properties on DOC bioreactivity in 15 Swedish catchments spanning a large geographical and environmental gradient. We found that the short-term bioreactive pools (0-6 d of decay experiments) were linked to high aquatic primary productivity that, in turn, was stimulated by phosphorus loading from forested, agricultural and urban areas. Unexpectedly, the percentage of long-term bioreactive DOC (determined in 1-year experiments) increased with WRT, possibly due to photo-transformation of recalcitrant DOC from terrestrial sources into long-term bioreactive DOC with relatively lower aromaticity. Thus, despite overall decreases in DOC during water transit through the inland water continuum, DOC becomes relatively more bioreactive on a long time-scale. This increase in DOC bioreactivity with increasing WRT along the freshwater continuum has previously been overlooked. Further studies are needed to explain the processes and mechanisms behind this pattern on a molecular level.

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 score0.059
Threshold uncertainty score0.551

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 studies0.0000.000
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 head0.005
GPT teacher head0.189
Teacher spread0.183 · 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

Citations53
Published2019
Admission routes1
Has abstractyes

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