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Record W2543967139 · doi:10.4095/295754

A synthesis of knowledge of the Milk River Transboundary Aquifer (Alberta, Canada - Montana, USA)

2015· report· en· W2543967139 on OpenAlexaffabout
Marie-Amélie Pétré, Alfonso Rivera

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsAquiferHydrology (agriculture)Water resource managementEnvironmental scienceGeographyGeologyGroundwaterGeotechnical engineering

Abstract

fetched live from OpenAlex

This report is part of the Milk River Transboundary Aquifer Project (MiRTAP) initiated by the Geological Survey of Canada in 2009. The objective of this report is to integrate information from previous geological, hydrogeological and geochemical studies of the Milk River Aquifer with data from the current study in order to develop an integrated dataset for study of the aquifer. The present report constitutes a comprehensive review of previous and current studies of the Milk River aquifer on both sides of the Canada/US border. It is a synthesis of knowledge on the aquifer as per 2015. A transboundary extent of the aquifer has been defined, and an integrated stratigraphic study has been carried out in order to correlate differently named but chrono-stratigraphically and depositionally equivalent Formations and members on both sides of the international border. The transboundary integration and development of unified stratigraphic model allows a better understanding of the aquifer. It will be used to generating a conceptual hydrogeological model to support development of a three-dimensional numeric hydrogeological model. It is anticipated that the transboundary numeric groundwater model will aid in improved water management and contribute to improved understanding of the sustainability of the groundwater resource.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0120.022
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.052
GPT teacher head0.248
Teacher spread0.197 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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