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Record W3010699531

The Possible Contribution of Chlorella vulgaris to Uptake High Concentration Carbon Dioxide to Albertaandrsquo;s Climate Leadership Plan.

2019· article· en· W3010699531 on OpenAlexaboutno aff
Seiede Samira Hosseini, Chu Angus

Bibliographic record

VenueJurnal Teknik Lingkungan : Electronic Journal of Civil and Environmental Engineering · 2019
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsnot available
Fundersnot available
KeywordsPhotobioreactorChlorella vulgarisCarbon dioxideCarbon sequestrationWastewaterEffluentTotal inorganic carbonEnvironmental sciencePulp and paper industryBiomass (ecology)Environmental engineeringCarbon fixationBotanyBiologyEngineeringEcologyAlgae
DOInot available

Abstract

fetched live from OpenAlex

In November 2015, the Government of Alberta proposed the Climate Leadership Plan (CLP). It aims to reduce carbon emissions while diversifying Alberta’s economy and protecting the health and the environment. In this research, the possibility of the contribution of CLP and using Chlorella vulgaris (C. vulgaris) in different biomass concentration to sequester CO2 in the domestic wastewater effluent as the Medium is assessed. Wastewater is an appropriate, renewable, economical medium, which contains enough nutrients. After cultivation of stock solution to 5% (v/v) CO2 the CO2 sequestration rate by C. vulgaris was measured in the presence of 20% (v/v) CO2 in the total volume of photobioreactor headspace. C. vulgaris illustrated the ability of fixation of 20% CO2 in the total gas volume of the photobioreactor headspace in optimal physical conditions in 35 hours. The CO2 fixation rate was higher in the middle and end of the exponential growth phase compared to other growth phases.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.128

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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 designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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