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Phosphorus and DOC availability influence the partitioning between bacterioplankton production and respiration in tidal marsh ecosystems

2012· article· en· W1604938671 on OpenAlexafffund
Paul A. del Giorgio, Roger E. I. Newell

Bibliographic record

VenueEnvironmental Microbiology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversité du Québec à Montréal
FundersNational Research Council Canada
KeywordsBacterioplanktonBiologyEcosystemRespirationPhosphorusMarshEcologyNutrientPhytoplanktonBotanyWetland

Abstract

fetched live from OpenAlex

The organic carbon consumed by aquatic bacteria (BCC) is partitioned between bacterial production (BP) and respiration (BR), but the factors that determine BCC and its partition into BP and BR are not well understood. We explored the coupling between BR, BR and BCC, and their links to dissolved organic carbon (DOC) and nutrient availability in natural and restored tidal marshes and in the adjoining waters of Delaware Bay estuary. Labile DOC (LDOC) ranged from 3% to 22% of the DOC pool, and explained more of the variance in both BR and BCC than did bulk DOC. Bacterial growth efficiency (BGE) was highly variable (0.09-0.58), and natural Spartina alterniflora marshes had consistently higher BGE than both restoration marshes and tidal floodwaters. BGE was negatively related to the ratio of LDOC to total dissolved phosphorous, which was highest in natural marshes. The enhancement of BP observed in the marshes relative to the estuarine floodwaters had different origins: In natural marshes it was mostly due to increases in BGE, whereas in restored marshes it followed increased BCC. These results highlight the importance of P in regulating microbial metabolism in coastal areas, and the need to understand the pathways that lead to BP in these systems.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.712

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 studies0.0000.001
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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

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