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Record W7027552583

Crystalline Analysis of Geomicrobially-Induced Calcium Carbonate Precipitation in Sands using a Surface Percolation Treatment Technique

2022· dissertation· en· W7027552583 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of North Florida Digital Commons (University of North Florida) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationCalcium carbonateCalciumAmmonium carbonatePercolation (cognitive psychology)CarbonateMagnesium
DOInot available

Abstract

fetched live from OpenAlex

Ottawa 50/70 sand specimens and natural beach sand samples were treated using bio-augmented geomicrobies via a surface percolation technique. Testing was conducted on these specimens to determine how resultant calcium carbonate precipitation changed as a function of temperature, depth from the surface, and in the presence of magnesium. Specifically, x-ray Diffraction (XRD), a Scanning Electron Microscope (SEM), and Energy Dispersive X-ray Spectroscopy (EDS) were used to determine and quantify the presence of calcium carbonate and its associated phase. Results showed a direct relationship between temperature and precipitated calcium carbonate. In addition, as an unintended consequence associated with the treatment, ammonium chloride was produced. This ammonium chloride appears to mostly have formed upon the specimens’ surfaces, and its quantity appears to be inversely proportional to the quantity of precipitated calcium carbonate. This result has important implications in upscaling in the sense that the results imply that there may be some benefits associated with reducing ammonium chloride precipitation during the microbial reaction. The addition of magnesium led to inconclusive results in the sense that very little calcification was observed during this portion of the study.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.222
Teacher spread0.205 · 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.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

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