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Record W4387695165 · doi:10.3897/aca.6.e108163

Hydrogen and Dark Oxygen drive Microbial Productivity in diverse Groundwater Ecosystems

2023· article· en· W4387695165 on OpenAlexaffabout
Steven W. Ruff, Pauline Humez, Isabella Hrabě de Angelis, Muhe Diao, Michael Nightingale, Sara Cho, Liam Connors, Olukayode Kuloyo, Alan Seltzer, Samuel Bowman, Scott D. Wankel, Cynthia McClain, Bernhard Mayer, Marc Strous

Bibliographic record

VenueARPHA Conference Abstracts · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsAlberta Biodiversity Monitoring InstituteUniversity of Calgary
Fundersnot available
KeywordsGroundwaterAquiferEnvironmental chemistryEcosystemIsotopes of oxygenEnvironmental scienceOxygenMethaneChemistryGeochemistryEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Around 50% of humankind relies on groundwater as a source of drinking water. We investigated the age, geochemistry, and microbiology of 138 groundwater samples from 87 monitoring wells (<250 m depth) located in 14 aquifers in Canada (Fig. 1). Geochemistry and microbiology showed consistent trends suggesting large-scale aerobic and anaerobic hydrogen, methane, nitrogen, and sulfur cycling carried out by diverse microbial communities. Older groundwaters, especially in aquifers with organic carbon-rich strata, contained on average more cells than younger groundwaters, challenging current estimates of subsurface cell abundances. We observed substantial concentrations of dissolved oxygen in older groundwaters that could support aerobic lifestyles in subsurface ecosystems at an unprecedented scale. Metagenomics, oxygen isotope analyses and mixing models indicated that “dark oxygen” was produced in situ via microbial dismutation. We show that ancient groundwaters sustain productive communities and highlight an overlooked oxygen source in present and past subsurface ecosystems of Earth Ruff et al. 2023). Title, Abstract, and Figure 1 are reproduced from (Ruff et al. 2023) without adaptations, according to the terms of http://creativecommons.org/licenses/by/4.0/.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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)0.0010.002

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 head0.019
GPT teacher head0.221
Teacher spread0.202 · 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.

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
Published2023
Admission routes2
Has abstractyes

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