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Record W2790209677 · doi:10.4095/306611

Groundwater-surface water interactions: who cares and why?

2018· report· en· W2790209677 on OpenAlexaffabout
M J Hinton, H A J Russell

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGroundwaterSurface waterEnvironmental scienceHydrology (agriculture)Water resource managementGeologyEnvironmental engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Groundwater and surface water have different characteristics. They are both part of the hydrologic cycle and there are various hydrologic transfers between them. These hydrological processes also influence biogeochemical and biological processes. Groundwater-surface water (GW-SW) interactions are the combination of these processes and their effects. GW-SW interactions generally occur at small scales and are spatially and temporally variable but they can influence flow, chemistry and biology at large scales. Therefore, GW-SW interactions affect many environmental and societal issues related to water quantity, water quality, aquatic ecosystems and watershed management. Science can support management of these issues by determining where and when GW-SW interactions are significant, and what impact they have at larger scales. Partners in the southern Ontario project of the Groundwater Geoscience Program are working to better understand and characterize GW-SW interactions in Southern Ontario through integrated GW-SW modelling, GW-SW interaction framework development and aerial thermal infra-red reconnaissance surveys.

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)0.007
metaresearch head score (Gemma)0.030
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.439
Threshold uncertainty score0.873

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.012
Scholarly communication0.0070.010
Open science0.0020.003
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0100.003

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 head0.026
GPT teacher head0.270
Teacher spread0.244 · 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
Published2018
Admission routes2
Has abstractyes

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