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Record W3107154335 · doi:10.1139/er-2020-0093

Geochemical tracers in submarine groundwater discharge research: practice and challenges from a view of climate changes

2020· article· en· W3107154335 on OpenAlexvenueno aff
Shan Jiang, J. Severino P. Ibánhez, Ying Wu, Jing Zhang

Bibliographic record

VenueEnvironmental Reviews · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsSubmarine groundwater dischargeEnvironmental scienceGroundwaterKarstOceanographyEutrophicationBenthic zoneHydrology (agriculture)SubmarineGeologyEarth scienceEcologyAquiferNutrient

Abstract

fetched live from OpenAlex

Submarine groundwater discharge (SGD), the flux of porewater from permeable seabed or karst conduits to surface water bodies, delivers a significant quantity of land-borne solutes to coastal oceans. This input of land-derived solutes is frequently linked with eutrophication, harmful algae blooms, and benthic hypoxia, and hence has the potential to trigger great economic losses. Geophysical and geochemical tracers, including salinity, temperature, water stable isotopes, and radioactive elements, have been widely applied in SGD studies for more than 50 years to, amongst others, identify water sources, estimate residence times, and quantify discharge rates. Here we review advantages and shortcomings of these tracers in the study of SGD. Application requirements are outlined based on previous research and combined tracer approaches in karst environments, permeable coasts, and estuaries are illustrated under the view of climate changes. Current challenges with the use of geochemical tracers in SGD studies are highlighted and opportunities to develop these tracers for improved coastal management showcased.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.094
GPT teacher head0.274
Teacher spread0.180 · 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.

Study designNot applicable
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

Citations29
Published2020
Admission routes1
Has abstractyes

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