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Record W2059922227 · doi:10.1021/es035126l

Climate Effects on Stream Nitrate Concentrations at 16 Forested Catchments in South Central Ontario

2004· article· en· W2059922227 on OpenAlexaffabout
Shaun A. Watmough, M. Catherine Eimers, Julian Aherne, Peter J. Dillon

Bibliographic record

VenueEnvironmental Science & Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsTrent University
FundersMinistry of Environment
KeywordsSTREAMSWetlandHydrology (agriculture)Environmental scienceSoil waterNitrateSaturation (graph theory)Deposition (geology)Drainage basinStructural basinEcologySoil scienceGeologyGeography

Abstract

fetched live from OpenAlex

Increased nitrate (NO3) concentrations in streamwaters draining forested catchments are reportedly an early indicator of nitrogen (N) saturation. Nitrate concentrations in streams draining 16 forested catchments in south central Ontario were monitored over a 16-year period, during which time N bulk deposition was relatively constant (approximately 9 kg ha(-1) yr(-1)). Mean annual NO3 concentrations in streams were both highly variable among catchments and among years, although patterns of annual concentration were similar among many catchments. Coherence analysis identified two stream groupings. Shallow soils, moderate slopes, low NO3 concentration, and a large wetland component characterized the first group. The second group had primarily upland characteristics including deeper soils, steeper slopes, higher NO3 concentrations, and a much smaller wetland component. Patterns in NO3 concentration in wetland-influenced streams appeared to be related to summer drought and cumulative frost depth, whereas NO3 concentrations in upland-draining streams appeared to be related to both mean annual air temperature and summer drought. Because a number of different climate parameters as well as the physical character of the catchments apparently influence NO3 export, NO3 concentrations in streams are not a good indicator of N saturation in this region.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.004
GPT teacher head0.188
Teacher spread0.185 · 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 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

Citations79
Published2004
Admission routes2
Has abstractyes

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