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Record W1496857805 · doi:10.4319/lom.2009.7.21

A sea‐going continuous culture system for investigating phytoplankton community response to macro‐ and micro‐nutrient manipulations

2009· article· en· W1496857805 on OpenAlexaff
Lisa D. Pickell, Mark L. Wells, Charles G. Trick, William P. Cochlan

Bibliographic record

VenueLimnology and Oceanography Methods · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsWestern University
FundersU.S. Department of EnergyNational Science Foundation
KeywordsPhytoplanktonOceanographyBiomass (ecology)Environmental scienceNitratePlanktonNutrientEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Continuous cultures can provide refined insights on the response of phytoplankton communities to small changes in nutrient flux, however are logistically more challenging to perform at sea than batch cultures. Here we describe the design and successful testing of a new continuous culture system for shipboard experiments using natural phytoplankton communities. Using this system, we studied the effects of nitrate amendments in coastal waters of the Pacific Northwest, and low‐level iron additions in High Nitrate, Low Chlorophyll (HNLC) waters of the subarctic Pacific. With nitrate amendments the coastal phytoplankton community showed proportional increases in chlorophyll biomass and appeared to achieve dynamic steady state as biomass, nutrient drawdown, and photo‐physiology stabilized after 4 d. In contrast, biomass did not reach steady state with iron amendment in the 10‐d HNLC experiment, however a major transition in dominant phytoplankton from small autotrophic flagellates to the toxigenic diatom Pseudo‐nitzschia was observed. This new continuous culture design demonstrated high precision in flow rates, good mixing within culture vessels, and was simple to operate at sea. This system provides an effective platform for investigating small changes in macro‐ and micro‐nutrient flux on the growth of individual phytoplankton species and, in turn, the trajectory of planktonic ecosystems.

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)0.002
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 score0.111
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.291
Teacher spread0.264 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

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