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Record W2087487147 · doi:10.1073/pnas.0910579107

Iron enrichment stimulates toxic diatom production in high-nitrate, low-chlorophyll areas

2010· article· en· W2087487147 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsPhytoplanktonDiatomNitrateEcosystemChlorophyllChlorophyll aEnvironmental scienceDomoic acidFood webAlgal bloomMarine ecosystemBiologyOceanographyNutrientEcologyEnvironmental chemistryChemistryBotany

Abstract

fetched live from OpenAlex

Oceanic high-nitrate, low-chlorophyll environments have been highlighted for potential large-scale iron fertilizations to help mitigate global climate change. Controversy surrounds these initiatives, both in the degree of carbon removal and magnitude of ecosystem impacts. Previous open ocean enrichment experiments have shown that iron additions stimulate growth of the toxigenic diatom genus Pseudonitzschia. Most Pseudonitzschia species in coastal waters produce the neurotoxin domoic acid (DA), with their blooms causing detrimental marine ecosystem impacts, but oceanic Pseudonitzschia species are considered nontoxic. Here we demonstrate that the sparse oceanic Pseudonitzschia community at the high-nitrate, low-chlorophyll Ocean Station PAPA (50 degrees N, 145 degrees W) produces approximately 200 pg DA L(-1) in response to iron addition, that DA alters phytoplankton community structure to benefit Pseudonitzschia, and that oceanic cell isolates are toxic. Given the negative effects of DA in coastal food webs, these findings raise serious concern over the net benefit and sustainability of large-scale iron fertilizations.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.280
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