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Record W4392581692 · doi:10.5194/egusphere-egu24-12381

Assessing the biodegradability of dissolved organic carbon in freshwater systems: A method evaluation study

2024· preprint· en· W4392581692 on OpenAlexaff
D. Green, Fereidoun Rezanezhad, D. Scott Smith, Stephanie Slowinski, Philippe Van Cappellen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsBiodegradationDissolved organic carbonTotal organic carbonEnvironmental scienceCarbon fibersEnvironmental chemistryChemistryComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Dissolved organic carbon (DOC) is an important contributor to both carbon cycling and other biogeochemical processes in aquatic ecosystems. The biodegradable fraction of DOC can be microbially degraded over time, producing carbon dioxide (CO2), a greenhouse gas. In addition, microbial degradation-resistant DOC can accumulate in water bodies, causing chemical and physical changes to aquatic systems. Although biodegradable DOC (BDOC) is widely studied, there is no agreed-upon standard method for assessing its biodegradability. Here, we aimed to develop and evaluate a new method for determining BDOC in freshwater samples. Our method includes filtering water samples to below 0.22 µm, to remove existing microbial cells, prior to inoculating the samples with a concentrated microbial inoculum produced by stepwise isolation of microbial cells from a peat sample. In addition, we added solutions containing nitrogen and phosphorus (in the forms of NH4NO3 and K2HPO4, respectively) to ensure that the microbes were not nutrient-limited. The samples were then capped with foam stoppers and incubated in the dark at 25⁰C on a shaker for 28 days to allow constant aeration during BDOC degradation. When applied to five freshwater samples collected from rivers, stormwater ponds, and a lake, and a glucose control, we observed that the amount of BDOC in the natural samples ranged from 15% to 53% and was 90% in the glucose control. Rates of BDOC degradation were calculated from DOC measurements at six sampling time points between days 0 and 28. We found that the DOC trends with time were best explained by two successive phases for BDOC degradation in all of the samples: an initial, fast, phase of BDOC degradation followed by a second, slower, phase of BDOC degradation where the rate constant for the second phase was between 5.57 and 565 times slower than for the initial phase. Changes in chemical characteristics of DOC measured using absorbance and fluorescence parameters including specific ultraviolet absorbance at 254 nm (SUVA254), humification index (HIX), and parallel factor analysis (PARAFAC) at each sampling time revealed that the initial, fast, phase of BDOC degradation often represents the utilization of small, non-aromatic compounds while the later, slower, phase of BDOC degradation often represents the utilization of more complex, aromatic compounds. The presented method provides a new approach to measure and characterize BDOC degradability and degradation kinetics that can be applied to future studies on biogeochemical processes in aquatic ecosystems.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.387
Teacher spread0.315 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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