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Record W2101562216 · doi:10.1139/f02-153

Nitrate stable isotopes: tools for determining nitrate sources among different land uses in the Mississippi River Basin

2002· article· en· W2101562216 on OpenAlexvenueno aff
Cecily C. Y. Chang, Carol Kendall, Steven R. Silva, William A. Battaglin, Donald H. Campbell

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
FundersU.S. Geological Survey
KeywordsNitrateStable isotope ratioStructural basinEnvironmental scienceHydrology (agriculture)Drainage basinLand useδ15NLivestockIsotope analysisIsotopes of nitrogenManureEcologyδ13CGeographyGeologyBiology

Abstract

fetched live from OpenAlex

A study was conducted to determine whether NO3– stable isotopes (δ15N and δ18O), at natural abundance levels, could discriminate among NO3– sources from sites with different land uses at the basin scale. Water samples were collected from 24 sites in the Mississippi River Basin from five land-use categories: (1) large river basins (>34 590 km2) draining multiple land uses and smaller basins in which the predominant land use was (2) urban, (3) undeveloped, (4) crops, or (5) crops and livestock. Our data suggest that riverine nitrates from different land uses have overlapping but moderately distinct isotopic signatures. δ18O data were critical in showing abrupt changes in NO3– source with discharge. The isotopic values of large rivers resembled crop sites, sites with livestock tended to have δ15N values characteristic of manure, and urban sites tended to have high δ18O values characteristic of atmospheric nitrate.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.040
GPT teacher head0.198
Teacher spread0.158 · 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

Citations175
Published2002
Admission routes1
Has abstractyes

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