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Record W2969422986 · doi:10.1504/ijgw.2019.10023358

Bioremediation of minkery wastewater and astaxanthin production by <i>Haematococcus pluvialis</i>

2019· article· en· W2969422986 on OpenAlex
İlhami Yıldız, Yu Liu

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

VenueInternational Journal of Global Warming · 2019
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHaematococcus pluvialisPluvialisAstaxanthinWastewaterChemistryBiomass (ecology)Food sciencePulp and paper industryNitrogenBotanyBiologyEnvironmental engineeringEnvironmental scienceAgronomyOrganic chemistryCarotenoid

Abstract

fetched live from OpenAlex

A two-stage system was designed for culturing and induction processes of H. pluvialis. H. pluvialis was cultivated in minkery wastewater and compared with the conventional Bold's basal medium, and grew better in diluted (1.5%) minkery wastewater, yielding a biomass production of 906.3 ± 34.0 mg L−1. Total nitrogen and total phosphorus were also removed successfully. In the following induction stage, nitrogen-deprived vegetative cells were exposed to high light intensity for astaxanthin production, and the resultant production was 39.72 ± 1.69 mg L−1. Employing the diluted wastewater, a mixotrophic induction strategy was also tested by using a series of acetate and NaCl concentrations. The findings indicated that the optimal combination for astaxanthin production was 38.14 mM acetate and 0.58% (w/v) NaCl. And the optimal astaxanthin concentration was 67.95 ± 3.93 mg L−1 after a 12-day induction period. This study concludes that H. pluvialis offers a potential opportunity for treating minkery wastewater and producing high-value astaxanthin.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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