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Record W26311034 · doi:10.1093/toxsci/kfv169

Sources and Biochemical Composition of Detrital Organic Matter In theSea

2009· article· en· W26311034 on OpenAlexfundno aff
Karl Kaiser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsOrganic matterComposition (language)Environmental scienceChemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

Detrital organic matter in the oceans is one of the largest and most dynamic reservoirs of reactive organic carbon on Earth. Despite the importance of this reservoir in the global cycle of carbon and associated bioreactive elements, not much is known about its sources, chemical composition and diagenetic processing. In this project we (1) use a novel multi-biomarker approach to trace bacterial remnants and provide estimates of bacterial contribution to marine organic matter, (2) investigate the chemical composition and diagenetic processing of suspended POM (>100 nm), high-molecular-weight dissolved organic matter (HMW DOM, 1-100 nm) and low-molecular-weight dissolved organic matter (LMW DOM, <1 nm), and (3) explore the composition and remineralization of semi-labile DOM in the upper mesopelagic (110-300 m) zone. Samples were collected at the US Joint Global Ocean Flux Study Program (JGOFS) time-series stations near Bermuda (BATS) and Hawaii (HOT) and analyzed for organic carbon, organic nitrogen, D- and L-amino acids, neutral sugars and amino sugars. A high-throughput microwave-assisted vapor-phase hydrolysis method was developed to measure D- and L-amino acids in seawater samples. Bacterial detritus was a major component of particulate organic matter (POM) and is an important source of submicron particles and colloids in the ocean. Peptidoglycan was a substantial component of POM but not of dissolved organic matter (DOM). Compositional differences between POM and DOM primarily reflected the selective incorporation of specific bacterial components into these reservoirs. Autotrophic and heterotrophic bacterial sources were not quantified separately, but the presence of D-aspartic acid (D-Asx) and D-serine (D-Ser) suggested that heterotrophic sources were substantial. The average reactivity of bacterial organic matter was comparable to that of the bulk organic carbon pool. Bacteria were important sources of labile, semilabile and refractory dissolved organic carbon. Bacterial organic matter accounted for ∼25 % of particulate and dissolved organic carbon and ∼50 % of particulate and dissolved organic nitrogen. These results demonstrate the importance of bacteria in regulating the ocean carbon and nitrogen cycles. Concentrations of amino acids, neutral sugar and amino sugars in unfiltered seawater sharply declined with depth at both stations, indicating an upper ocean source and rapid turnover of these components. Carbohydrates and amino acids were major reactive components of semi-labile DOM in the upper mesopelagic zone. The size distribution of organic matter was heavily skewed to smaller molecular sizes. Depth comparisons showed that larger size classes of organic matter were more efficiently removed than smaller size classes. Carbon-normalized yields of amino acids, neutral sugars and amino sugars decreased rapidly with depth and molecular size. Together these biochemicals accounted for 55% of organic carbon in surface POM but only 2% of the organic carbon in LMW DOM in deep water. Chemical compositions showed distinct differences between organic matter size classes indicating the extent of diagenetic processing increased with decreasing molecular size. These findings are consistent with the size-reactivity continuum model for organic matter in which bioreactivity decreases with decreasing molecular size and diagenetic processes lead to the formation of smaller components that are resistant to biodegradation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.003
GPT teacher head0.186
Teacher spread0.182 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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