MétaCan
Menu
Back to cohort
Record W4416697875 · doi:10.5194/bg-22-3931-2025

Hot-spring inputs and climate drive dynamic shifts in archaeal communities in Lake Magadi, Kenya Rift Valley

2025· article· en· W4416697875 on OpenAlexaff
Evan R. Collins, Troy Ferland, Isla S. Castañeda, R. Bernhart Owen, Tim K. Lowenstein, Andrew S. Cohen, Robin W. Renaut, Molly D. O’Beirne, Josef P. Werne

Bibliographic record

VenueBiogeosciences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsUniversity of Saskatchewan
FundersDivision of Behavioral and Cognitive SciencesDivision of Earth SciencesUniversity of MinnesotaNational Science Foundation
Keywordsδ13CMethaneHydrothermal circulationMethanogenesisSedimentary rockOrganic matterIsotopes of carbonClimate changeRift

Abstract

fetched live from OpenAlex

Abstract. The methane index (MI) is an organic geochemical index that uses isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs) as a proxy for methane cycling. Here, we report results from sediments in core MAG14-2A that span almost 500 ka in Lake Magadi, Kenya. The deposits show abrupt shifts between high and low MI values through calcareous, tuffaceous and zeolitic silts. The MI switches “off” (MI < 0.2) and “on” (MI > 0.5) through the core with bulk organic matter enriched in 13C during “MI-off” periods (∼ −18 ‰) in the upper part of the core, whereas 13C is lower (−22 ‰ to −25 ‰) in lower parts of the sedimentary sequence. Sediments deposited when the MI switches “on” showed δ13COM values as low as −89.4 ‰, but most values were within the range of −28 ‰ to −30 ‰, which is consistent with contributions from methanogens rather than methanotrophs. Thus, the likely source of these high MI values in Lake Magadi is methanogenic archaea. Our results show that hydrothermal inputs of bicarbonate-rich waters into Lake Magadi combined with further evaporative concentration cause a shift in the dominant archaeal communities, alternating between two stable states.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.986

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBiogeosciencesSame topicAquatic Ecosystems and BiodiversityFrench-language works237,207