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Record W2101509673 · doi:10.1139/e03-067

Partitioning of bacterial communities between travertine depositional facies at Mammoth Hot Springs, Yellowstone National Park, U.S.A.

2003· article· en· W2101509673 on OpenAlexvenueno aff
Bruce W. Fouke, George T. Bonheyo, Beth Sanzenbacher, Jorge Frias‐Lopez

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersAmerican Chemical Society Petroleum Research FundNational Science Foundation
KeywordsSedimentary depositional environmentFaciesGeologyHot springMammothTufaSpring (device)GeochemistryPaleontologyCarbonateChemistry

Abstract

fetched live from OpenAlex

A culture-independent molecular survey indicates that the composition of bacterial communities is distinctly partitioned between travertine depositional facies in the surface drainage system of Spring AT-1 at Angel Terrace, Mammoth Hot Springs, Yellowstone National Park. PCR (polymerase chain reaction) amplification and sequencing of 16S rRNA genes with universally conserved bacterial primers has identified over 553 unique partial and 104 complete gene sequences (derived from more than 14 000 clones), affiliated with 221 unique species that represent 21 bacterial divisions. These sequences exhibited < 12% similarity in bacterial community composition between each of the travertine depositional facies. This implies that relatively little downstream bacterial transport and colonization took place despite the rapid and continuous flow of spring water from the high-temperature to low-temperature facies. These results suggest that travertine depositional facies, which are independently determined by the physical and chemical conditions of the hot spring drainage system, effectively predict bacterial community composition as well as the morphology and chemistry of travertine precipitation.

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 score0.203
Threshold uncertainty score0.987

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.032
GPT teacher head0.230
Teacher spread0.198 · 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

Citations81
Published2003
Admission routes1
Has abstractyes

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