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Record W2807433195 · doi:10.1038/s41396-018-0149-2

Decrypting the sulfur cycle in oceanic oxygen minimum zones

2018· article· en· W2807433195 on OpenAlexaff
Sean A. Crowe, Raymond P. Cox, CarriAyne Jones, David A. Fowle, J F Santibañez-Bustos, Osvaldo Ulloa, Donald E. Canfield

Bibliographic record

VenueThe ISME Journal · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British Columbia
FundersNational Research FoundationAgouron InstituteDanmarks GrundforskningsfondVillum Fonden
KeywordsBiologySulfurSulfur cycleOxygenPaleontologyBacteria

Abstract

fetched live from OpenAlex

Abstract Here we present ecophysiological studies of the anaerobic sulfide oxidizers considered critical to cryptic sulfur cycling in oceanic oxygen minimum zones (OMZs). We find that HS− oxidation rates by microorganisms in the Chilean OMZ offshore from Dichato are sufficiently rapid (18 nM h−1), even at HS− concentrations well below 100 nM, to oxidize all sulfide produced during sulfate reduction in OMZs. Even at 100 nM, HS− is well below published half-saturation concentrations and we conclude that the sulfide-oxidizing bacteria in OMZs (likely the SUP05/ARTIC96BD lineage of the gammaproteobacteria) have high-affinity (>105 g−1 wet cells h−1) sulfur uptake systems. These specific affinities for sulfide are higher than those recorded for any other organism on any other substrate. Such high affinities likely allow anaerobic sulfide oxidizers to maintain vanishingly low sulfide concentrations in OMZs driving marine cryptic sulfur cycling. If more broadly distributed, such high-affinity sulfur biochemistry could facilitate sulfide-based metabolisms and prominent S-cycles in many other ostensibly sulfide-free environments.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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