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Record W2325140869 · doi:10.1071/aseg2012ab298

Elucidating the nature of surface water - groundwater interactions beneath a large unregulated river system with the aid of AEM data

2012· article· en· W2325140869 on OpenAlexaff
Tim Munday, Andrew Fitzpatrick, Kevin Cahill, Glenn A. Harrington

Bibliographic record

VenueASEG Extended Abstracts · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCameco (Canada)
Fundersnot available
KeywordsAquiferGroundwaterHydrogeologyGeologyAlluviumHydrology (agriculture)TransectSurface waterDrainage basinStructural basinEnvironmental scienceGeomorphologyOceanographyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

SummaryThe Fitzroy River with its associated alluvial aquifer in northern Western Australia has been considered in planning for Perth’s future water supply and for agricultural development but its potential needs to be informed by detailed understanding of groundwatersurface water interactions occurring along its extent, and in particular must consider and account for the consequences that might arise when extracting groundwater from shallow and deep aquifers linked to this river system. Results from the interpretation of a regional scale longitudinal transect (~274 line kms) of SkyTEM helicopter EM data are presented which elucidate river-bed processes occurring along its extent. The AEM indicate a variable groundwater quality and related aquifer characteristics associated with different parts of the river. A 1D laterally constrained inversion (LCI), was examined against hydrochemical, environmental tracer (including 222Rn and Cl-), and hydrogeological data sampled longitudinally. Combined, they indicate a link between the Fitzroy’s alluvial aquifer system and with underlying Canning Basin sediments. The results demonstrate the value of regional, reconnaissance scale AEM surveys to better define groundwater processes beneath large unregulated river systems.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.290

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 studies0.0000.000
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 head0.019
GPT teacher head0.253
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2012
Admission routes1
Has abstractyes

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