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Metabolic diversity of heterotrophic bacterioplankton over winter and spring in the coastal Arctic Ocean

2008· article· en· W2121741494 on OpenAlexaffabout
M. Montserrat Sala, Ramón Terrado, Connie Lovejoy, Fernando Unrein, Carlos Pedrós‐Alió

Bibliographic record

VenueEnvironmental Microbiology · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité Laval
FundersGeneralitat de Catalunya
KeywordsBacterioplanktonPhytoplanktonSpring bloomBiologyArcticOceanographyDissolved organic carbonChlorophyll aOrganic matterPelagic zoneBayPlanktonEcologyNutrientBotany

Abstract

fetched live from OpenAlex

Metabolic diversity of heterotrophic bacterioplankton was tracked from early winter through spring with Biolog Ecoplates under the seasonally ice covered arctic shelf in the Canadian Arctic (Franklin Bay, Beaufort Sea). Samples were taken every 6 days from December 2003 to May 2004 at the surface, the halocline where a temperature inversion occurs, and at 200 m, close to the bottom. Despite the low nutrient levels and low chlorophyll a, suggesting oligotrophy in the winter surface waters, the number of substrates used (NSU) was greater than in spring, when chlorophyll a concentrations increased. Denaturing gradient gel electrophorisis analysis also indicated that the winter and spring bacterial communities were phylogenetically distinct, with several new bands appearing in spring. In spring, the bacterial community would have access to the freshly produced organic carbon from the early phytoplankton bloom and the growth of rapidly growing specialist phenotypes would be favoured. In contrast, in winter bacterioplankton consumed more complex organic matter originated during the previous year's phytoplankton production. At the other depths we tested the NSU was similar to that for the winter surface, with no seasonal pattern. Instead, bacterioplankton metabolism seemed to be influenced by resuspension, advection, and sedimentation events that contributed organic matter that enhanced bacterial metabolism.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.690

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)0.0010.000

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 head0.007
GPT teacher head0.153
Teacher spread0.146 · 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

Citations76
Published2008
Admission routes2
Has abstractyes

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