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Record W2171430685 · doi:10.1016/j.femsec.2005.03.014

Cultivation factors and population size control the uptake of nitrogen by the microalgae Chlorella vulgaris when interacting with the microalgae growth-promoting bacterium Azospirillum brasilense

2005· article· en· W2171430685 on OpenAlexaff
Luz E. de‐Bashan, Hani Antoun, Yoav Bashan

Bibliographic record

VenueFEMS Microbiology Ecology · 2005
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsUniversité Laval
FundersCentro de Investigaciones Biológicas del NoroesteConsejo Nacional de Ciencia y Tecnología
KeywordsChlorella vulgarisAzospirillum brasilenseBiologyPopulationNitrogenAmmoniumBacteriaBiomass (ecology)NitrateMicrobial inoculantChlorellaBotanyAlgaeFood scienceEcologyChemistry

Abstract

fetched live from OpenAlex

Growth of and the capacity to take up nitrogen in the freshwater microalgae Chlorella vulgaris were studied while varying the concentrations of ammonium and nitrate, the pH and the source of carbon in a synthetic wastewater growth medium when co-immobilized in alginate beads with the microalgae growth-promoting bacterium Azospirillum brasilense. Analyses of 29 independent experiments showed that co-immobilization of the microalgae with A. brasilense could result in two independent phenomena directly affected by cultivation factors, such as nitrogen species, pH and presence of a carbon source. First, growth of the microalgal population increased without an increase in the capacity of the single cells to take up nitrogen, or second, the capacity of cells to take up nitrogen increased without an increase of the total microalgal population. These phenomena were dependent on the population density of the microalgae, which was in turn affected by cultivation factors. This supports the conclusion that the size of the microalgal population controls the uptake of nitrogen in C. vulgaris cells - the higher the population (regardless the experimental parameters), the less nitrogen each cell takes up.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations93
Published2005
Admission routes1
Has abstractyes

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