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Record W2019114115 · doi:10.1021/es500344s

A Greenhouse-Scale Photosynthetic Microbial Bioreactor for Carbon Sequestration in Magnesium Carbonate Minerals

2014· article· en· W2019114115 on OpenAlexafffundabout
Jenine McCutcheon, Ian Power, Anna L. Harrison, Gregory M. Dipple, Gordon Southam

Bibliographic record

VenueEnvironmental Science & Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of British ColumbiaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCarbon Management Canada
KeywordsCarbonateCarbon sequestrationCarbonationCarbonate mineralsEnvironmental chemistryChemistryTailingsMagnesiumEnvironmental scienceNutrientCarbon dioxide

Abstract

fetched live from OpenAlex

A cyanobacteria dominated consortium collected from an alkaline wetland located near Atlin, British Columbia, Canada accelerated the precipitation of platy hydromagnesite [Mg5(CO3)4(OH)2·4H2O] in a linear flow-through experimental model wetland. The concentration of magnesium decreased rapidly within 2 m of the inflow point of the 10-m-long (∼1.5 m(2)) bioreactor. The change in water chemistry was monitored over two months along the length of the channel. Carbonate mineralization was associated with extra-cellular polymeric substances in the nutrient-rich upstream portion of the bioreactor, while the lower part of the system, which lacked essential nutrients, did not exhibit any hydromagnesite precipitation. A mass balance calculation using the water chemistry data produced a carbon sequestration rate of 33.34 t of C/ha per year. Amendment of the nutrient deficiency would intuitively allow for increased carbonation activity. Optimization of this process will have application as a sustainable mining practice by mediating magnesium carbonate precipitation in ultramafic mine tailings storage facilities.

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 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 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 score0.117
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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 teacher head, 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

Citations55
Published2014
Admission routes3
Has abstractyes

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