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Record W2071923798 · doi:10.2134/jeq2004.1440

Sources and Transformations of Nitrogen Compounds along the Lower Jordan River

2004· article· en· W2071923798 on OpenAlexaff
Michal Segal‐Rozenhaimer, Uri Shavit, Avner Vengosh, Ittai Gavrieli, Efrat Farber, Ran Holtzman, Bernhard Mayer, Avi Shaviv

Bibliographic record

VenueJournal of Environmental Quality · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Calgary
FundersUnited States Agency for International Development
KeywordsNitrateEnvironmental scienceHydrology (agriculture)SewageNitrificationRiver deltaAmmoniumNitrogenDeltaEnvironmental engineeringGeologyEcologyChemistry

Abstract

fetched live from OpenAlex

The Lower Jordan River is located in the semiarid area of the Jordan Valley, along the border between Israel and Jordan. The implementation of the water sections of the peace treaty between Israel and Jordan and the countries' commitment to improve the ecological sustainability of the river system require a better understanding of the riverine environment. This paper investigates the sources and transformations of nitrogen compounds in the Lower Jordan River by applying a combination of physical, chemical, isotopic, and mathematical techniques. The source waters of the Lower Jordan River contain sewage, which contributes high ammonium loads to the river. Ammonium concentrations decrease from 20 to 0-5 mg N L(-1) along the first 20 km of the Lower Jordan River, while nitrate concentrations increase from nearly zero to 10-15 mg N L(-1), and delta(15)N (NO(3)) values increase from less than 5 per thousand to 15-20 per thousand. Our data analysis indicates that intensive nitrification occurs along the river, between 5 and 12 km from the Sea of Galilee, while further downstream nitrate concentration increases mostly due to an external subsurface water source that enters the river.

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)0.000
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 score0.025
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.225
Teacher spread0.214 · 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

Citations29
Published2004
Admission routes1
Has abstractyes

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