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Record W2116703650 · doi:10.5194/bg-11-3707-2014

Revisiting the disappearance of terrestrial dissolved organic matter in the ocean: a <i>δ</i> <sup>13</sup> C study

2014· article· en· W2116703650 on OpenAlexafffund
Karine Lalonde, Anssi V. Vähätalo, Yves Gélinas

Bibliographic record

VenueBiogeosciences · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsConcordia University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesAcademy of FinlandNatural Sciences and Engineering Research Council of CanadaHelsingin Yliopisto
KeywordsColored dissolved organic matterDissolved organic carbonPhotobleachingEnvironmental chemistryTRACERChemistryOceanographyEnvironmental scienceGeologyFluorescencePhytoplankton

Abstract

fetched live from OpenAlex

Abstract. Organic carbon (OC) depleted in 13C is a widely used tracer for terrestrial organic matter (OM) in aquatic systems. Photochemical reactions can, however, change δ13C of dissolved organic carbon (DOC) when chromophoric, aromatic-rich terrestrial OC is selectively mineralized. We assessed the robustness of the δ13C signature of DOC (δ13CDOC) as a tracer for terrestrial OM by estimating its change during the photobleaching of chromophoric DOM (CDOM) from 10 large rivers. These rivers cumulatively account for approximately one-third of the world's freshwater discharge to the global ocean. Photobleaching of CDOM by simulated solar radiation was associated with the photochemical mineralization of 16 to 43% of the DOC and, by preferentially removing compounds depleted in 13C, caused a 1 to 2.9‰ enrichment in δ13C in the residual DOC. Such solar-radiation-induced photochemical isotopic shift could bias the calculations of terrestrial OM discharge in coastal oceans towards the marine end-member. Shifts in terrestrial δ13CDOC should be taken into account when constraining the terrestrial end-member in global calculation of terrestrially derived DOM in the world ocean.

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)0.002
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 score0.012
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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

Citations50
Published2014
Admission routes2
Has abstractyes

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