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Record W7019237274

Geochemistry of Naturally Occurring Methane from Two Canadian Case Studies

2019· dissertation· en· W7019237274 on OpenAlexaboutno aff

Bibliographic record

VenueThe Knowledge Bank (The Ohio State University) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHydrocarbonMethaneNatural gasGroundwaterBrineFossil fuelNoble gasEnergy source
DOInot available

Abstract

fetched live from OpenAlex

Increased unconventional hydrocarbon production in the United States and Canada piqued interest in natural gas development, while simultaneously raising concerns regarding changing groundwater quality following hydrocarbon extraction. Adequate characterization of baseline groundwater geochemistry preceded hydrocarbon production in many areas. This study, based in the relatively undeveloped St. Edouard region of Quebec, Canada, and the moderately developed McCully gas field of New Brunswick, Canada, attempts to characterize naturally occurring hydrocarbon geochemistry prior to unconventional energy development. Specifically, I focus on characterizing the composition and origins of hydrocarbons and salts in regional groundwater in these areas. Eighteen samples were collected (eleven from St. Edouard and seven from McCully) and analyzed for hydrocarbon concentrations and hydrocarbon stable isotopic composition, major gas concentrations, noble gas elemental abundance and isotopic concentrations, tritium concentrations and dissolved ion concentrations. The data suggests that hydrocarbons represent a mixture of thermogenic natural gas and biogenic methane likely formed by primary and secondary biogenesis; noble gas geochemistry supports these conclusions. The ionic composition of water is consistent with a diluted brine associated with the thermogenic hydrocarbon gases that also showed signs of post genetic alteration via hydrocarbon oxidation.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.219
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 designQualitative
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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