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Record W2062405278 · doi:10.1130/g34194.1

Influence of bedrock mineral composition on microbial diversity in a subglacial environment

2013· article· en· W2062405278 on OpenAlex
Andrew C. Mitchell, Melissa J. Lafrenière, Mark Skidmore, Eric S. Boyd

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsQueen's University
Fundersnot available
KeywordsBedrockDiversity (politics)Library scienceState (computer science)Composition (language)ArchaeologyGeographyGeologySociologyArtAnthropologyGeomorphologyComputer science

Abstract

fetched live from OpenAlex

Microorganisms in subglacial environments drive the chemical weathering of bedrock; however, the influence of bedrock mineralogy on the composition and activity of microbial assemblages in such environments is poorly understood. Here, using a combination of in situ mineral incubation and DNA fingerprinting techniques, we demonstrate that pyrite is the dominant mineralogical control on subglacial bacterial community structure and composition. In addition, we show that the abundance of Fe in the incubated minerals influences the development of mineral-associated biomass. The ubiquitous nature of pyrite in many common bedrock types and high SO42– concentrations in most glacial meltwaters suggest that pyrite may be a dominant lithogenic control on microbial communities in many subglacial systems. Mineral-based energy may therefore serve a fundamental role in sustaining subglacial microbial populations and enabling their persistence over glacial-interglacial time scales.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.998

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.007
GPT teacher head0.201
Teacher spread0.193 · 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