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Record W2088441735 · doi:10.1623/hysj.54.4.754

Can the impacts of climate change on groundwater resources be studied without the use of transient models?

2009· article· en· W2088441735 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHydrological Sciences Journal · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAquiferGroundwaterClimate changeEnvironmental sciencePrecipitationTransient (computer programming)Groundwater flowWater resourcesConstraint (computer-aided design)Hydrology (agriculture)Water resource managementComputer scienceGeologyMeteorologyEngineeringGeographyEcology

Abstract

fetched live from OpenAlex

Abstract Burgeoning cities and a changing climate impose extraordinary stresses on the management of global water supplies. Groundwater flow models can greatly assist pro-active decision making, but their practical application to date has been largely limited to steady-state scenarios that conveniently ignore aquifer storage. In future, aquifer models will prove invaluable for optimizing the use of groundwater resources and confronting the impacts of climate change, but they must be run in “transient mode” that fully incorporates time-variant inflows and outflows. This will impose additional data demands in the form of reliable specific yield values, together with good temporal information on precipitation, potential evaporation, groundwater levels and streamflows. Lack of adequate field data to “fuel” predictive models is emerging as the greatest constraint on tackling groundwater problems caused by climate change.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.101
GPT teacher head0.256
Teacher spread0.155 · 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