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Record W2909542171 · doi:10.4095/299733

Studies on potential links between deep shale formations and shallow aquifers in Quebec and New Brunswick

2017· report· en· W2909542171 on OpenAlexaffabout
Christine Rivard

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsOil shaleGeologyAquiferMining engineeringGeochemistrySeismologyPetroleum engineeringPaleontologyGroundwaterGeotechnical engineering

Abstract

fetched live from OpenAlex

The Geological Survey of Canada is carrying out two projects to assess potential fluid migration pathways from deep (~2 km) shale or tight sand units to shallow aquifers in southern Quebec (St-Édouard area, St. Lawrence Lowlands) and in southern New Brunswick (Sussex area, McCully gas field). The geological and hydrogeological contexts of these study areas are very different. One of the main differences is the aquifer types, which are mainly composed of organic-rich black shales in St-Édouard and dominated by sandstone in the Sussex area. Also, while no shale gas well is in production in the St. Lawrence Lowlands, the McCully gas field has been in production since 2001. Because the intermediate zone, located between aquifers and reservoirs, is poorly known, these projects are using multi-source direct and indirect data including field geology, geophysics, geomechanics, hydrogeology and water and rock geochemistry to evaluate aquifer vulnerability.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.075
GPT teacher head0.298
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes2
Has abstractyes

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