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Record W2132918635 · doi:10.4319/lo.2001.46.7.1802

Iron uptake and physiological response of phytoplankton during a mesoscale Southern Ocean iron enrichment

2001· article· en· W2132918635 on OpenAlexaff
María T. Maldonado, Philip W. Boyd, Julie LaRoche, Robert F. Strzepek, Anya M. Waite, Andrew R. Bowie, Peter Croot, Russell Frew, Neil M. Price

Bibliographic record

VenueLimnology and Oceanography · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of British ColumbiaMcGill University
FundersNational Institute of Water and Atmospheric ResearchUniversity of Otago
KeywordsPhytoplanktonNitrateEnvironmental chemistryPlanktonMesoscale meteorologyBloomSeawaterOceanographyChemistryGenetic algorithmNutrientBiologyEcologyGeology

Abstract

fetched live from OpenAlex

Iron supply is thought to regulate primary production in high nitrate, low chlorophyll (HNLC) regions of the sea in both the past and the present. A critical aspect of this relationship is acquisition of iron (Fe) by phytoplankton, which occurs through a complex series of extracellular reactions that are influenced by Fe chemistry and speciation. During the first in situ mesoscale Fe‐enrichment experiment in the Southern Ocean (Southern Ocean iron release experiment [SOIREE]), we monitored the uptake of Fe by three size classes of plankton and their ensuing physiological response to the Fe enrichment. Rates of Fe uptake from both inorganic Fe (Fe′) and organic Fe complexes (FeL) were initially fast, indicative of Fe‐limitation. After Fe enrichment phytoplankton down‐regulated Fe uptake and optimized physiological performance, but by day 12 they had greatly increased their capacity to acquire Fe from FeL. The increase in Fe uptake from FeL coincided with a sixfold decrease in Fe′ that followed the production of Fe‐binding organic ligands. Phytoplankton were able to use organically bound Fe at rates sufficient to maintain net growth for more than 42 d. Adaptation to such shifts in Fe chemistry may contribute to bloom longevity in these polar HNLC waters.

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 score0.004
Threshold uncertainty score0.466

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)0.0000.000

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 head0.010
GPT teacher head0.192
Teacher spread0.183 · 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

Citations87
Published2001
Admission routes1
Has abstractyes

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