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Record W2892867310 · doi:10.1126/science.aau0382

Biosynthesis of the neurotoxin domoic acid in a bloom-forming diatom

2018· article· en· W2892867310 on OpenAlexafffund
John K. Brunson, Shaun M. K. McKinnie, Jonathan R. Chekan, John P. McCrow, Zachary D. Miles, Erin M. Bertrand, Vincent A. Bielinski, Hanna Luhavaya, Miroslav Obornı́k, G. Jason Smith, David A. Hutchins, Andrew E. Allen, Bradley S. Moore

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsDalhousie University
FundersNational Institute of Environmental Health SciencesNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaGrantová Agentura České RepublikyGordon and Betty Moore FoundationU.S. Department of EnergyNational Institutes of HealthNational Science Foundation
KeywordsDomoic acidNeurotoxinDiatomAlgal bloomToxinMarine toxinAlgaeBiologyBloomEcologyBiochemistryPhytoplankton

Abstract

fetched live from OpenAlex

How algae turn tides toxic Algal blooms can devastate marine mammal communities through the production of neurotoxins that accumulate within the food web. Brunson et al. identified a cluster of genes associated with biosynthesis of the neurotoxin domoic acid in a marine diatom (see the Perspective by Pohnert et al. ). In vitro experiments established a series of enzymes that create the core structure of the toxin. Knowledge of the genes involved in domoic acid production will allow for genetic monitoring of algal blooms and aid in identifying conditions that trigger toxin production. Science , this issue p. 1356 ; see also p. 1308

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.255
Teacher spread0.247 · 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 designBench or experimental
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

Citations216
Published2018
Admission routes2
Has abstractyes

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