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Record W1903788792 · doi:10.1139/cjpp-2012-0249

Management of oxidative stress by microalgae

2013· review· en· W1903788792 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2013
Typereview
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsNOSM UniversityLaurentian University
Fundersnot available
KeywordsOxidative stressAntioxidantCatalaseSuperoxide dismutaseReactive oxygen speciesGlutathione reductaseOxidative phosphorylationBiochemistryChemistryBiologyGlutathione peroxidase

Abstract

fetched live from OpenAlex

The aim of this review is to provide an overview of the current research on oxidative stress in eukaryotic microalgae and the antioxidant compounds microalgae utilize to control oxidative stress. With the potential to exploit microalgae for the large-scale production of antioxidants, interest in how microalgae manage oxidative stress is growing. Microalgae can experience increased levels of oxidative stress and toxicity as a result of environmental conditions, metals, and chemicals. The defence mechanisms for microalgae include antioxidant enzymes such as superoxide dismutase, catalase, peroxidases, and glutathione reductase, as well as non-enzymatic antioxidant molecules such as phytochelatins, pigments, polysaccharides, and polyphenols. Discussed herein are the 3 areas the literature has focused on, including how conditions stress microalgae and how microalgae respond to oxidative stress by managing reactive oxygen species. The third area is how beneficial microalgae antioxidants are when administered to cancerous mammalian cells or to rodents experiencing oxidative stress.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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