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Record W2103501127 · doi:10.1111/1462-2920.13063

Heterotrophic bacteria from an extremely phosphate‐poor lake have conditionally reduced phosphorus demand and utilize diverse sources of phosphorus

2015· article· en· W2103501127 on OpenAlexaff
Mengyin Yao, Felix J. Elling, CarriAyne Jones, Sulung Nomosatryo, Christopher P. Long, Sean A. Crowe, Maciek R. Antoniewicz, Kai‐Uwe Hinrichs, Julia A. Maresca

Bibliographic record

VenueEnvironmental Microbiology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of British Columbia
FundersLembaga Ilmu Pengetahuan IndonesiaDanmarks GrundforskningsfondNational Institute of General Medical SciencesGottfried Wilhelm Leibniz Universität HannoverDeutsche ForschungsgemeinschaftUniversity of Delaware
KeywordsHeterotrophPhosphorusBiologyPhosphateActinobacteriaNutrientBacteriaEcologyBotanyBiochemistryChemistry

Abstract

fetched live from OpenAlex

Heterotrophic Proteobacteria and Actinobacteria were isolated from Lake Matano, Indonesia, a stratified, ferruginous (iron-rich), ultra-oligotrophic lake with phosphate concentrations below 50 nM. Here, we describe the growth of eight strains of heterotrophic bacteria on a variety of soluble and insoluble sources of phosphorus. When transferred to medium without added phosphorus (P), the isolates grow slowly, their RNA content falls to as low as 1% of cellular dry weight, and 86-100% of the membrane lipids are replaced with amino- or glycolipids. Similar changes in lipid composition have been observed in marine photoautotrophs and soil heterotrophs, and similar flexibility in phosphorus sources has been demonstrated in marine and soil-dwelling heterotrophs. Our results demonstrate that heterotrophs isolated from this unusual environment alter their macromolecular composition, which allows the organisms to grow efficiently even in their extremely phosphorus-limited environment.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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 designBench or experimental
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

Citations35
Published2015
Admission routes1
Has abstractyes

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