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Record W2401911887 · doi:10.1038/ismej.2015.215

Selective consumption and metabolic allocation of terrestrial and algal carbon determine allochthony in lake bacteria

2015· article· en· W2401911887 on OpenAlexafffund
François Guillemette, S. Leigh McCallister, Paul A. del Giorgio

Bibliographic record

VenueThe ISME Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of OttawaNational Science Foundation
KeywordsBacterioplanktonBiomass (ecology)BiologyAlgaeCarbon cycleDissolved organic carbonEcologyCarbon dioxideRespirationProductivityCarbon fibersEnvironmental chemistryHeterotrophSubstrate (aquarium)PhytoplanktonEcosystemBacteriaNutrientBotanyChemistry

Abstract

fetched live from OpenAlex

Here we explore strategies of resource utilization and allocation of algal versus terrestrially derived carbon (C) by lake bacterioplankton. We quantified the consumption of terrestrial and algal dissolved organic carbon, and the subsequent allocation of these pools to bacterial growth and respiration, based on the δ(13)C isotopic signatures of bacterial biomass and respiratory carbon dioxide (CO2). Our results confirm that bacterial communities preferentially remove algal C from the terrestrially dominated organic C pool of lakes, but contrary to current assumptions, selectively allocate this autochthonous substrate to respiration, whereas terrestrial C was preferentially allocated to biosynthesis. The results provide further evidence of a mechanism whereby inputs of labile, algal-derived organic C may stimulate the incorporation of a more recalcitrant, terrestrial C pool. This mechanism resulted in a counterintuitive pattern of high and relatively constant levels of allochthony (~76%) in bacterial biomass across lakes that otherwise differ greatly in productivity and external inputs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.361

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 studies0.0000.000
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 head0.027
GPT teacher head0.252
Teacher spread0.225 · 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

Citations145
Published2015
Admission routes2
Has abstractyes

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