MétaCan
Menu
Back to cohort
Record W2784099543 · doi:10.1038/s41396-017-0031-7

Phosphate insensitive aminophosphonate mineralisation within oceanic nutrient cycles

2018· article· en· W2784099543 on OpenAlexfundno aff
Jason P. Chin, John P. Quinn, John W. McGrath

Bibliographic record

VenueThe ISME Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersQueen's UniversityUniversität WienUniversity of CambridgeQueen's University BelfastDepartment for Employment and Learning, Northern Ireland
KeywordsBiologyRegulonPhosphorusNutrientPhosphateCatabolismMarine bacteriophageBacteriaEnzymeGeneBiochemistryBotanyEcologyGeneticsRegulation of gene expressionChemistry

Abstract

fetched live from OpenAlex

Many areas of the ocean are nutrient-poor yet support large microbial populations, leading to intense competition for and recycling of nutrients. Organic phosphonates are frequently found in marine waters, but require specialist enzymes for catabolism. Previous studies have shown that the genes that encode these enzymes in marine systems are under Pho regulon control and so are repressed by inorganic phosphate. This has led to the conclusion that phosphonates are recalcitrant in much of the ocean, where phosphorus is not limiting despite the degradative genes being common throughout the marine environment. Here we challenge this paradigm and show, for the first time, that bacteria isolated from marine samples have the ability to mineralise 2-aminoethylphosphonate, the most common biogenic marine aminophosphonate, via substrate-inducible gene regulation rather than via Pho-regulated metabolism. Substrate-inducible, Pho-independent 2-aminoethylphosphonate catabolism therefore represents a previously unrecognised component of the oceanic carbon, nitrogen and phosphorus cycles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.367

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 science0.0000.000
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 head0.263
Teacher spread0.252 · 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 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe ISME JournalSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207