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Record W2217057828 · doi:10.4138/atlgeol.2015.015

Atlantic Universities Geoscience Conference 2015: 65th Annual Conference, October 22-24, 2015

2015· article· en· W2217057828 on OpenAlexaffvenueabout
Chris White

Bibliographic record

VenueAtlantic Geology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsNova Scotia Department of Energy
Fundersnot available
KeywordsGeologyLibrary scienceEarth scienceOceanographyComputer science

Abstract

fetched live from OpenAlex

Many rock formations, particularly granitic rocks and sandstones of Devonian to Carboniferous age, may have an impact on the water quality of surrounding areas.Weathering and geochemical processes can mobilize uranium, which allows uranium to accumulate in ground water systems in concentrations above recommended guidelines established by Health Canada.It may also be possible that anthropogenic modifications can mobilize uranium from soil or rock.This study is focused on discovering the chemical agent or agents responsible for mobilizing uranium from uranium-bearing rocks in Nova Scotia.It is believed that both natural and anthropogenic causes may be behind uranium mobilization in some Nova Scotian locations.Road salt and sea water introduce ions into geologic formations that have a potential impact on uranium.Gypsum, either in the form of gyprock in construction waste or as naturally occurring geologic formations, introduces sulfate into bedrock and soil, which adds another variable to analyze.Ions such as chloride, calcium, sulphate, and bicarbonate will be analyzed in an attempt to isolate the variable (or variables) that mobilize uranium.A leaching experiment using ground rock samples and controlled extraction fluids will aim to isolate the geochemical process or processes responsible for the accumulation of uranium in Nova Scotia ground water.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.033
GPT teacher head0.238
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; both teacher heads agree on what is shown here.

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

Citations1
Published2015
Admission routes3
Has abstractyes

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