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Record W2948229821 · doi:10.1051/e3sconf/20199809014

Redox hydrogeochemistry of organic rich floodplain exemplified by Ammer river

2019· article· en· W2948229821 on OpenAlexaff
Evgenii Kortunov, Chuanhe Lu, Richard T. Amos, Peter Grathwohl

Bibliographic record

VenueE3S Web of Conferences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsCarleton University
Fundersnot available
KeywordsFloodplainNitrateEnvironmental scienceGroundwaterTotal organic carbonAquiferHydrology (agriculture)SedimentDissolved organic carbonWater qualitySulfateEnvironmental chemistryGeologyEcologyChemistry

Abstract

fetched live from OpenAlex

Diffusive groundwater pollution caused by agricultural and atmospheric inputs is a pressing issue in environmental management worldwide. Various researchers have studied nitrate contamination since the substantial increase of nitrogen fertilization in agriculture starting in the second half of the 20th century. This study addresses large scale reactive solute transport in typical landscapes and aquifers exemplified by geological analogues of southwestern Germany.. Fate of nitrate and other solutes (e.g. agricultural nitrate, ammonium, natural sulfate and dissolved organic carbon) was studied in a typical small river floodplain. Reactive transport model of Ammer river floodplain shows that agriculture nitrate is reduced rapidly in the Ammer floodplain sediments. However, there is a potential for geogenic production of ammonium in sediment layers high in organic carbon and peat, which might be a major source of nitrate in the drains. Part of the nitrate in drains and creeks in the Ammer valley thus could be of geogenic origin. Such findings are relevant for regional land and water quality management.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.184
Teacher spread0.178 · 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 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
Published2019
Admission routes1
Has abstractyes

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