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Record W2007736859 · doi:10.3389/fmicb.2013.00369

Putting the N in dinoflagellates

2013· review· en· W2007736859 on OpenAlexafffund
Steve Dagenais-Bellefeuille, David Morse

Bibliographic record

VenueFrontiers in Microbiology · 2013
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDinoflagellateTrophic levelMixotrophBiologyRed tideHeterotrophPhotosynthesisNutrientAlgaeNitrogen cycleEcologyAlgal bloomPlanktonPhytoplanktonNitrogenBotanyChemistryBacteria

Abstract

fetched live from OpenAlex

The cosmopolitan presence of dinoflagellates in aquatic habitats is now believed to be a direct consequence of the different trophic modes they have developed through evolution. While heterotrophs ingest food and photoautotrophs photosynthesize, mixotrophic species are able to use both strategies to harvest energy and nutrients. These different trophic modes are of particular importance when nitrogen nutrition is considered. Nitrogen is required for the synthesis of amino acids, nucleic acids, chlorophylls, and toxins, and thus changes in the concentrations of various nitrogenous compounds can strongly affect both primary and secondary metabolism. For example, high nitrogen concentration is correlated with rampant cell division resulting in the formation of the algal blooms commonly called red tides. Conversely, nitrogen starvation results in cell cycle arrest and induces a series of physiological, behavioral and transcriptomic modifications to ensure survival. This review will combine physiological, biochemical, and transcriptomic data to assess the mechanism and impact of nitrogen metabolism in dinoflagellates and to compare the dinoflagellate responses with those of diatoms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations157
Published2013
Admission routes2
Has abstractyes

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