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Record W2911349712 · doi:10.4095/313599

A renaissance in regional hydrogeology

2019· report· en· W2911349712 on OpenAlexaboutno aff
David L. Rudolph

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsThe RenaissanceHydrogeologyGeologyArtGeotechnical engineeringArt history

Abstract

fetched live from OpenAlex

Over the last five decades hydrogeologic research has evolved through a series of different core areas. The focus has changed based on significant scientific breakthroughs within the emerging discipline and on societal priorities that have influenced funding opportunities. In recent years, significant attention and research activity have refocussed on regional groundwater assessment, one of the foundational topics in modern hydrogeology. This renewed emphasis is related to a deterioration in the apparent resilience of groundwater systems due to chronic influences of legacy contaminants, overexploitation and increased variability within the water cycle. The recent water quantity crises in the USA and western Canada, and the Source Water Protection work underway in Ontario, have illustrated some of the significant impacts on regional groundwater resources within the North American context. Emerging sensor technology, modeling platforms and increasing access to large data sets is enhancing the understanding of key processes and providing unprecedented quantitative insight within the entire hydrologic cycle at the watershed scale. Challenges remain with the optimal integration of data streams into modeling platforms to ensure appropriate parameterization and minimize uncertainty associated with the results. In this presentation, the increasing emphasis on regional hydrogeologic assessment will be discussed within the context of how it may influence the Government awareness and prioritization of groundwater in Canada as a strategic, economic and yet vulnerable 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)0.009
metaresearch head score (Gemma)0.011
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: Other
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.007
Scholarly communication0.0070.007
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.056
GPT teacher head0.317
Teacher spread0.261 · 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
Published2019
Admission routes1
Has abstractyes

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