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Picky Eaters: Carbon isotopic evidence for the uniform bioavailability of riverine dissolved organic matter to a model marine microorganism

2024· preprint· en· W4393160596 on OpenAlexafffund
Lea Baumser, Lindsey Potts, Brett D. Walker, Nagissa Mahmoudi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of OttawaMcGill University
FundersCanada Research Chairs
KeywordsDissolved organic carbonEnvironmental chemistryBioavailabilityEnvironmental scienceOrganic matterCarbon cycleTotal organic carbonChemistryEcologyBiologyEcosystem

Abstract

fetched live from OpenAlex

Dissolved organic matter (DOM) is a key component of the global carbon cycle, with rivers delivering significant amounts of DOM to oceans. Urbanization and agricultural land-use alter the age and chemical composition of riverine DOM, which likely impact the downstream bioavailability of riverine DOM. Here, we use bioreactor incubations of a marine bacterium (Pseudoalteromonas sp. 3D05) to investigate DOM bioavailability from two distinct rivers: the Suwannee River (natural, non-urbanized), and the Upper Mississippi River Basin (anthropogenically influenced). We measured rates of microbial CO2 production and radiocarbon ages (as Δ14C) to assess bioavailable DOM remineralization. We observed nearly identical cell densities and degradation patterns for both riverine DOM incubations. Respired DOM Δ14C values were also similar and decreased over time indicative of preferential utilization of recently synthesized “modern” substrates. These findings reveal unexpected similarities in riverine DOM bioavailability, indicating similar short term biological reactivity despite large DOM compositional differences.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.035
GPT teacher head0.277
Teacher spread0.243 · 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
Published2024
Admission routes2
Has abstractyes

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